- title: 'Clusterability: A Theoretical Study'
abstract: 'We investigate measures of the clusterability of data sets. Namely, ways to define how ‘strong’ or ‘conclusive’ is the clustering structure of a given data set. We address this issue with generality, aiming for conclusions that apply regardless of any particular clustering algorithm or any specific data generation model. We survey several notions of clusterability that have been discussed in the literature, as well as propose a new notion of data clusterability. Our comparison of these notions reveals that, although they all attempt to evaluate the same intuitive property, they are pairwise inconsistent. Our analysis discovers an interesting phenomenon; Although most of the common clustering tasks are NP-hard, finding a close-to-optimal clustering for well-clusterable data sets is easy (computationally). We prove instances of this general claim with respect to the various clusterability notions that we discuss. Finally, we investigate how hard it is to determine the clusterability value of a given data set. In most cases, it turns out that this is an NP-hard problem.'
volume: 5
URL: http://proceedings.mlr.press/v5/ackerman09a.html
PDF: http://proceedings.mlr.press/v5/ackerman09a/ackerman09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-ackerman09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Ackerman
given: Margareta
- family: Ben-David
given: Shai
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 1-8
id: ackerman09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 1
lastpage: 8
published: 2009-04-15 00:00:00 +0000
- title: 'Latent Force Models'
abstract: 'Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modeling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from computational biology, motion capture and geostatistics.'
volume: 5
URL: http://proceedings.mlr.press/v5/alvarez09a.html
PDF: http://proceedings.mlr.press/v5/alvarez09a/alvarez09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-alvarez09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Alvarez
given: Mauricio
- family: Luengo
given: David
- family: Lawrence
given: Neil
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 9-16
id: alvarez09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 9
lastpage: 16
published: 2009-04-15 00:00:00 +0000
- title: 'Variational Bridge Regression'
abstract: 'Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments only to attain the variational distributions for the regression coefficients. By choosing specific values of hyper-parameters (tuning parameters) present in the model, we can mimic the model selection performance of best subset selection in sparse underlying settings. The fundamental difference between MAP, \emphmaximum a posteriori, estimation and the proposed method is that, here we can obtain approximate inferences besides a point estimator. We also empirically analyze the frequentist properties of the estimator obtained. Results suggest that the proposed method yields an estimator that performs significantly better in sparse underlying setups than the existing state-of-the-art procedures in both n>p and p>n scenarios.'
volume: 5
URL: http://proceedings.mlr.press/v5/armagan09a.html
PDF: http://proceedings.mlr.press/v5/armagan09a/armagan09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-armagan09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Armagan
given: Artin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 17-24
id: armagan09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 17
lastpage: 24
published: 2009-04-15 00:00:00 +0000
- title: 'Learning Low Density Separators'
abstract: 'We define a novel, basic, unsupervised learning problem - learning the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task is relevant to several problems in machine learning, such as semi-supervised learning and clustering stability. We investigate the question of existence of a universally consistent algorithm for this problem. We propose two natural learning paradigms and prove that, on input unlabeled random samples generated by any member of a rich family of distributions, they are guaranteed to converge to the optimal separator for that distribution. We complement this result by showing that no learning algorithm for our task can achieve uniform learning rates (that are independent of the data generating distribution).'
volume: 5
URL: http://proceedings.mlr.press/v5/ben-david09a.html
PDF: http://proceedings.mlr.press/v5/ben-david09a/ben-david09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-ben-david09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Ben-David
given: Shai
- family: Lu
given: Tyler
- family: Pal
given: David
- family: Sotakova
given: Miroslava
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 25-32
id: ben-david09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 25
lastpage: 32
published: 2009-04-15 00:00:00 +0000
- title: 'Supervised Spectral Latent Variable Models'
abstract: 'We present a probabilistic structured prediction method for learning input-output dependencies where correlations between outputs are modeled as low-dimensional manifolds constrained by both geometric, distance preserving output relations,and predictive power of inputs. Technically this reduces to learning a probabilistic, input conditional model, over latent (manifold) and output variables using an alternation scheme. In one round, we optimize the parameters of an input-driven manifold predictor using latent targets given by preimages (conditional expectations) of the current manifold-to-output model. In the next round, we use the distribution given by the manifold predictor in order to maximize the probability of the outputs with an additional, implicit distance preserving constraint on the manifold. The resulting Supervised Spectral Latent Variable Model (SSLVM) combines the properties of probabilistic geometric manifold learning (accommodates geometric constraints corresponding to any spectral embedding method including PCA, ISOMAP or Laplacian Eigenmaps), with the additional supervisory information to further constrain it for predictive tasks. We demonstrate the superiority of the method over baseline PPCA + regression frameworks and show its potential in difficult realworld computer vision benchmarks designed for the reconstruction of three-dimensional human poses from monocular image sequences.'
volume: 5
URL: http://proceedings.mlr.press/v5/bo09a.html
PDF: http://proceedings.mlr.press/v5/bo09a/bo09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-bo09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Bo
given: Liefeng
- family: Sminchisescu
given: Cristian
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 33-40
id: bo09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 33
lastpage: 40
published: 2009-04-15 00:00:00 +0000
- title: 'Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming'
abstract: 'We present a novel method for estimating tree-structured covariance matrices directly from observed continuous data. A representation of these classes of matrices as linear combinations of rank-one matrices indicating object partitions is used to formulate estimation as instances of well-studied numerical optimization problems. In particular, our estimates are based on projection, where the covariance estimate is the nearest tree-structured covariance matrix to an observed sample covariance matrix. The problem is posed as a linear or quadratic mixed-integer program (MIP) where a setting of the integer variables in the MIP specifies a set of tree topologies of the structured covariance matrix. We solve these problems to optimality using efficient and robust existing MIP solvers. We present a case study in phylogenetic analysis of expression in yeast gene families and a comparison using simulated data to distance-based tree estimating procedures.'
volume: 5
URL: http://proceedings.mlr.press/v5/bravo09a.html
PDF: http://proceedings.mlr.press/v5/bravo09a/bravo09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-bravo09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Bravo
given: Hector Corrada
- family: Wright
given: Stephen
- family: Eng
given: Kevin
- family: Keles
given: Sunduz
- family: Wahba
given: Grace
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 41-48
id: bravo09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 41
lastpage: 48
published: 2009-04-15 00:00:00 +0000
- title: 'A New Perspective for Information Theoretic Feature Selection'
abstract: 'Feature Filters are among the simplest and fastest approaches to feature selection. A “filter” defines a statistical criterion, used to rank features on how useful they are expected to be for classification. The highest ranking features are retained, and the lowest ranking can be discarded. A common approach is to use the Mutual Information between the features and class label. This area has seen a recent flurry of activity, resulting in a confusing variety of heuristic criteria all based on mutual information, and a lack of a principled way to understand or relate them. The contribution of this paper is a unifying theoretical understanding of such filters. In contrast to current methods which manually construct filter criteria with particular properties, we show how to naturally derive a space of possible ranking criteria. We will show that several recent contributions in the feature selection literature are points within this space, and that there exist many points that have never been explored.'
volume: 5
URL: http://proceedings.mlr.press/v5/brown09a.html
PDF: http://proceedings.mlr.press/v5/brown09a/brown09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-brown09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Brown
given: Gavin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 49-56
id: brown09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 49
lastpage: 56
published: 2009-04-15 00:00:00 +0000
- title: 'Structure Identification by Optimized Interventions'
abstract: 'We consider the problem of optimal experimental design in structure identification. Whereas standard approaches simply minimize Shannon’s entropy of the estimated parameter posterior, we show how to select between alternative model configurations, too. Our method specifies the intervention that makes an experiment capable of determining whether or not a particular configuration hypothesis is correct. This is performed by a novel clustering technique in approximated Bayesian parameter estimation for non-linear dynamical systems. The computation of the perturbation that minimizes the effective number of clusters in the belief state is constrained by the increase of the expected Kullback-Leibler divergence between the parameter prior and the posterior. This enables the disambiguation of persisting alternative explanations in cases where standard design systematically fails. Its applicability is illustrated with a biochemical Goodwin model, showing correct identification between multiple kinetic structures. We expect that our approach will prove useful especially for complex structures with reduced observability and multimodal posteriors.'
volume: 5
URL: http://proceedings.mlr.press/v5/busetto09a.html
PDF: http://proceedings.mlr.press/v5/busetto09a/busetto09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-busetto09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Busetto
given: Alberto Giovanni
- family: Buhmann
given: Joachim
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 57-64
id: busetto09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 57
lastpage: 64
published: 2009-04-15 00:00:00 +0000
- title: 'Online Inference of Topics with Latent Dirichlet Allocation'
abstract: 'Inference algorithms for topic models are typically designed to be run over an entire collection of documents after they have been observed. However, in many applications of these models, the collection grows over time, making it infeasible to run batch algorithms repeatedly. This problem can be addressed by using online algorithms, which update estimates of the topics as each document is observed. We introduce two related Rao-Blackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model – incremental Gibbs samplers and particle filters – and compare their runtime and performance to that of existing algorithms.'
volume: 5
URL: http://proceedings.mlr.press/v5/canini09a.html
PDF: http://proceedings.mlr.press/v5/canini09a/canini09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-canini09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Canini
given: Kevin
- family: Shi
given: Lei
- family: Griffiths
given: Thomas
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 65-72
id: canini09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 65
lastpage: 72
published: 2009-04-15 00:00:00 +0000
- title: 'Handling Sparsity via the Horseshoe'
abstract: 'This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior. The horseshoe prior is a member of the family of multivariate scale mixtures of normals, and is therefore closely related to widely used approaches for sparse Bayesian learning, including, among others, Laplacian priors (e.g. the LASSO) and Student-t priors (e.g. the relevance vector machine). The advantages of the horseshoe are its robustness at handling unknown sparsity and large outlying signals. These properties are justifed theoretically via a representation theorem and accompanied by comprehensive empirical experiments that compare its performance to benchmark alternatives.'
volume: 5
URL: http://proceedings.mlr.press/v5/carvalho09a.html
PDF: http://proceedings.mlr.press/v5/carvalho09a/carvalho09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-carvalho09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Carvalho
given: Carlos M.
- family: Polson
given: Nicholas G.
- family: Scott
given: James G.
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 73-80
id: carvalho09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 73
lastpage: 80
published: 2009-04-15 00:00:00 +0000
- title: 'Relational Topic Models for Document Networks'
abstract: 'We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efﬁcient inference and learning algorithms based on variational methods and evaluate the predictive performance of the RTM for large networks of scientiﬁc abstracts and web documents.'
volume: 5
URL: http://proceedings.mlr.press/v5/chang09a.html
PDF: http://proceedings.mlr.press/v5/chang09a/chang09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-chang09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Chang
given: Jonathan
- family: Blei
given: David
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 81-88
id: chang09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 81
lastpage: 88
published: 2009-04-15 00:00:00 +0000
- title: 'Probabilistic Models for Incomplete Multi-dimensional Arrays'
abstract: 'In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data.'
volume: 5
URL: http://proceedings.mlr.press/v5/chu09a.html
PDF: http://proceedings.mlr.press/v5/chu09a/chu09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-chu09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Chu
given: Wei
- family: Ghahramani
given: Zoubin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 89-96
id: chu09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 89
lastpage: 96
published: 2009-04-15 00:00:00 +0000
- title: 'On Partitioning Rules for Bipartite Ranking'
abstract: 'The purpose of this paper is to investigate the properties of partitioning scoring rules in the bipartite ranking setup. We focus on ranking rules based on scoring functions. General sufficient conditions for the AUC consistency of scoring functions that are constant on cells of a partition of the feature space are provided. Rate bounds are obtained for cubic histogram scoring rules under mild smoothness assumptions on the regression function. In this setup, it is shown how to penalize the empirical AUC criterion in order to select a scoring rule nearly as good as the one that can be built when the degree of smoothness of the regression function is known.'
volume: 5
URL: http://proceedings.mlr.press/v5/clemencon09a.html
PDF: http://proceedings.mlr.press/v5/clemencon09a/clemencon09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-clemencon09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Clemencon
given: Stephan
- family: Vayatis
given: Nicolas
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 97-104
id: clemencon09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 97
lastpage: 104
published: 2009-04-15 00:00:00 +0000
- title: 'Gaussian Margin Machines'
abstract: 'We introduce Gaussian Margin Machines (GMMs), which maintain a Gaussian distribu- tion over weight vectors for binary classiﬁcation. The learning algorithm for these machines seeks the least informative distribution that will classify the training data correctly with high probability. One formulation can be expressed as a convex constrained optimization problem whose solution can be represented linearly in terms of training instances and their inner and outer products, supporting kernelization. The algorithm has a natural PAC-Bayesian generalization bound. A preliminary evaluation on handwriting recognition data shows that our algorithm improves over SVMs for the same task. methods, we maintain a distribution over alternative weight vectors, rather than committing to a single speciﬁc one. However, these distributions are not derived by Bayes? rule. Instead, they represent our knowledge of the weights given constraints imposed by the training examples. Speciﬁcally, we use a Gaussian distribution over weight vectors with mean and covariance parameters that are learned from the training data. The learning algorithm seeks for a distribu- tion with a small Kullback-Leibler (KL) divergence from a ﬁxed isotropic distribution, such that each training exam- ple is correctly classiﬁed by a strict majority of the weight vectors. Conceptually, this is a large-margin probabilistic principle, instead of the geometric large margin principle in SVMs. The learning problem for GMMs can be expressed as a convex constrained optimization, and its optimal solution'
volume: 5
URL: http://proceedings.mlr.press/v5/crammer09a.html
PDF: http://proceedings.mlr.press/v5/crammer09a/crammer09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-crammer09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Crammer
given: Koby
- family: Mohri
given: Mehryar
- family: Pereira
given: Fernando
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 105-112
id: crammer09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 105
lastpage: 112
published: 2009-04-15 00:00:00 +0000
- title: 'Learning Thin Junction Trees via Graph Cuts'
abstract: 'Structure learning algorithms usually focus on the compactness of the learned model. However, compact representation does not imply the existence of tractable inference algorithms: both exact and approximate inference may still be NP-hard. This focus on compactness leads to learning good models that require approximate inference techniques, thus reducing their prediction quality. In this paper, we propose a method for learning an attractive class of models: bounded treewidth junction trees. Those models permit both compact representation of probability distributions and efficient exact inference. Our method uses a new global criterion to construct the tree. Our criterion, based on a Bethe approximation of the likelihood, transforms the structure learning problem into an intuitive graph theoretical task. We present an efficient randomized algorithm with theoretical guarantees for finding good separators. We recursively apply this procedure to obtain a thin junction tree. Our extensive empirical evaluation demonstrates the benefit of applying exact inference using our models to answer queries. We also extend our technique to learn low tree-width conditional random fields, and demonstrate significant improvements over state of the art block-L1 regularization techniques.'
volume: 5
URL: http://proceedings.mlr.press/v5/dafna09a.html
PDF: http://proceedings.mlr.press/v5/dafna09a/dafna09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-dafna09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Dafna
given: Shahaf
- family: Guestrin
given: Carlos
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 113-120
id: dafna09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 113
lastpage: 120
published: 2009-04-15 00:00:00 +0000
- title: 'Matching Pursuit Kernel Fisher Discriminant Analysis'
abstract: 'We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant Analysis (MPKFDA). We provide generalisation error bounds analogous to those constructed for the Robust Minimax algorithm together with a sample compression bounding technique. We present experimental results on real world datasets, which show that MPKFDA is competitive with the KFDA and the SVM on UCI datasets, and additional experiments that show that the MPKFDA on average outperforms KFDA and SVM in extremely high dimensional settings.'
volume: 5
URL: http://proceedings.mlr.press/v5/diethe09a.html
PDF: http://proceedings.mlr.press/v5/diethe09a/diethe09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-diethe09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Diethe
given: Tom
- family: Hussain
given: Zakria
- family: Hardoon
given: David
- family: Shawe-Taylor
given: John
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 121-128
id: diethe09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 121
lastpage: 128
published: 2009-04-15 00:00:00 +0000
- title: 'Statistical and Computational Tradeoffs in Stochastic Composite Likelihood'
abstract: 'Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. We prove the consistency of the estimators, provide formulas for their asymptotic variance and computational complexity, and discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators in achieving a predefined balance between computational complexity and statistical accuracy.'
volume: 5
URL: http://proceedings.mlr.press/v5/dillon09a.html
PDF: http://proceedings.mlr.press/v5/dillon09a/dillon09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-dillon09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Dillon
given: Joshua
- family: Lebanon
given: Guy
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 129-136
id: dillon09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 129
lastpage: 136
published: 2009-04-15 00:00:00 +0000
- title: 'Variational Inference for the Indian Buffet Process'
abstract: 'The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.'
volume: 5
URL: http://proceedings.mlr.press/v5/doshi09a.html
PDF: http://proceedings.mlr.press/v5/doshi09a/doshi09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-doshi09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Doshi
given: Finale
- family: Miller
given: Kurt
- family: Gael
given: Jurgen Van
- family: Teh
given: Yee Whye
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 137-144
id: doshi09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 137
lastpage: 144
published: 2009-04-15 00:00:00 +0000
- title: 'Choosing a Variable to Clamp'
abstract: 'In this paper we propose an algorithm for approximate inference on graphical models based on belief propagation (BP). Our algorithm is an approximate version of Cutset Conditioning, in which a set of variables is instantiated to make the rest of the graph singly connected. We relax the constraint of single-connectedness, and select variables one at a time for conditioning, running belief propagation after each selection. We consider the problem of determining the best variable to clamp at each level of recursion, and propose a fast heuristic which applies backpropagation to the BP updates. We demonstrate that the heuristic performs better than selecting variables at random, and give experimental results which show that it performs competitively with existing approximate inference algorithms.'
volume: 5
URL: http://proceedings.mlr.press/v5/eaton09a.html
PDF: http://proceedings.mlr.press/v5/eaton09a/eaton09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-eaton09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Eaton
given: Frederik
- family: Ghahramani
given: Zoubin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 145-152
id: eaton09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 145
lastpage: 152
published: 2009-04-15 00:00:00 +0000
- title: 'The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training'
abstract: 'Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a kind of regularizer. We show the influence of architecture depth, model capacity, and number of training examples.'
volume: 5
URL: http://proceedings.mlr.press/v5/erhan09a.html
PDF: http://proceedings.mlr.press/v5/erhan09a/erhan09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-erhan09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Erhan
given: Dumitru
- family: Manzagol
given: Pierre-Antoine
- family: Bengio
given: Yoshua
- family: Bengio
given: Samy
- family: Vincent
given: Pascal
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 153-160
id: erhan09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 153
lastpage: 160
published: 2009-04-15 00:00:00 +0000
- title: 'Semi-Supervised Affinity Propagation with Instance-Level Constraints'
abstract: 'Recently, affinity propagation (AP) was introduced as an unsupervised learning algorithm for exemplar based clustering. Here we extend the AP model to account for semi-supervised clustering. AP, which is formulated as inference in a factor-graph, can be naturally extended to account for ?instance-level? constraints: pairs of data points that cannot belong to the same cluster (cannot-link), or must belong to the same cluster (must-link). We present a semi-supervised AP algorithm (SSAP) that can use instance-level constraints to guide the clustering. We demonstrate the applicability of SSAP to interactive image segmentation by using SSAP to cluster superpixels while taking into account user instructions regarding which superpixels belong to the same object. We demonstrate SSAP can achieve better performance compared to other semi-supervised methods.'
volume: 5
URL: http://proceedings.mlr.press/v5/givoni09a.html
PDF: http://proceedings.mlr.press/v5/givoni09a/givoni09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-givoni09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Givoni
given: Inmar
- family: Frey
given: Brendan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 161-168
id: givoni09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 161
lastpage: 168
published: 2009-04-15 00:00:00 +0000
- title: 'Multi-Manifold Semi-Supervised Learning'
abstract: 'We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a novel application of Hellinger distance and size-constrained spectral clustering. Experiments demonstrate the benefit of our multi-manifold semi-supervised learning approach.'
volume: 5
URL: http://proceedings.mlr.press/v5/goldberg09a.html
PDF: http://proceedings.mlr.press/v5/goldberg09a/goldberg09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-goldberg09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Goldberg
given: Andrew
- family: Zhu
given: Xiaojin
- family: Singh
given: Aarti
- family: Xu
given: Zhiting
- family: Nowak
given: Robert
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 169-176
id: goldberg09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 169
lastpage: 176
published: 2009-04-15 00:00:00 +0000
- title: 'Residual Splash for Optimally Parallelizing Belief Propagation'
abstract: 'As computer architectures move towards parallelism we must build a new theoretical understanding of parallelism in machine learning. In this paper we focus on parallelizing message passing inference algorithms in graphical models. We develop a theoretical understanding of the limitations of parallelism in belief propagation and bound the optimal achievable running parallel performance on a certain class of graphical models. We demonstrate that the fully synchronous parallelization of belief propagation is highly inefficient. We provide a new parallel belief propagation which achieves optimal performance on a certain class of graphical models. Using two challenging real-world problems, we empirically evaluate the performance of our algorithm. On the real-world problems, we find that our new algorithm achieves near linear performance improvements and out performs alternative parallel belief propagation algorithms.'
volume: 5
URL: http://proceedings.mlr.press/v5/gonzalez09a.html
PDF: http://proceedings.mlr.press/v5/gonzalez09a/gonzalez09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-gonzalez09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Gonzalez
given: Joseph
- family: Low
given: Yucheng
- family: Guestrin
given: Carlos
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 177-184
id: gonzalez09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 177
lastpage: 184
published: 2009-04-15 00:00:00 +0000
- title: 'Sparse Probabilistic Principal Component Analysis'
abstract: 'Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through adding an L1 regularizer. In this paper, we introduce a probabilistic formulation for sparse PCA. By presenting sparse PCA as a probabilistic Bayesian formulation, we gain the benefit of automatic model selection. We examine three different priors for achieving sparsification: (1) a two-level hierarchical prior equivalent to a Laplacian distribution and consequently to an L1 regularization, (2) an inverse-Gaussian prior, and (3) a Jeffrey’s prior. We learn these models by applying variational inference. Our experiments verify that indeed our sparse probabilistic model results in a sparse PCA solution.'
volume: 5
URL: http://proceedings.mlr.press/v5/guan09a.html
PDF: http://proceedings.mlr.press/v5/guan09a/guan09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-guan09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Guan
given: Yue
- family: Dy
given: Jennifer
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 185-192
id: guan09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 185
lastpage: 192
published: 2009-04-15 00:00:00 +0000
- title: 'Visualization Databases for the Analysis of Large Complex Datasets'
abstract: 'Comprehensive visualization that preserves the information in a large complex dataset requires a visualization database (VDB): many displays, some with many pages, and with one or more panels per page. A single display using a specific display method results from partitioning the data into subsets, sampling the subsets, and applying the method to each sample, typically one per panel. The time of the analyst to generate a display is not increased by choosing a large sample over a small one. Displays and display viewers can be designed to allow rapid scanning. Often, it is not necessary to view every page of a display. VDBs, already successful just with off-the-shelf tools, can be greatly improved by research that rethinks all of the areas of data visualization in the context of VDBs.'
volume: 5
URL: http://proceedings.mlr.press/v5/guha09a.html
PDF: http://proceedings.mlr.press/v5/guha09a/guha09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-guha09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Guha
given: Saptarshi
- family: Kidwell
given: Paul
- family: Hafen
given: Ryan P.
- family: Cleveland
given: William S.
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 193-200
id: guha09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 193
lastpage: 200
published: 2009-04-15 00:00:00 +0000
- title: 'Active Learning as Non-Convex Optimization'
abstract: 'We propose a new view of active learning algorithms as optimization. We show that many online active learning algorithms can be viewed as stochastic gradient descent on non-convex objective functions. Variations of some of these algorithms and objective functions have been previously proposed without noting this connection. We also point out a connection between the standard min-margin offline active learning algorithm and non-convex losses. Finally, we discuss and show empirically how viewing active learning as non-convex loss minimization helps explain two previously observed phenomena: certain active learning algorithms achieve better generalization error than passive learning algorithms on certain data sets and on other data sets many active learning algorithms are prone to local minima.'
volume: 5
URL: http://proceedings.mlr.press/v5/guillory09a.html
PDF: http://proceedings.mlr.press/v5/guillory09a/guillory09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-guillory09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Guillory
given: Andrew
- family: Chastain
given: Erick
- family: Bilmes
given: Jeff
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 201-208
id: guillory09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 201
lastpage: 208
published: 2009-04-15 00:00:00 +0000
- title: 'Network Completion and Survey Sampling'
abstract: 'We study the problem of learning the topology of an undirected network by observing a random subsample. Specifically, the sample is chosen by randomly selecting a fixed number of vertices, and for each we are allowed to observe all edges it is incident with. We analyze a general formalization of learning from such samples, and derive confidence bounds on the number of differences between the true and learned topologies, as a function of the number of observed mistakes and the algorithm’s bias. In addition to this general analysis, we also analyze a variant of the problem under a stochastic block model assumption.'
volume: 5
URL: http://proceedings.mlr.press/v5/hanneke09a.html
PDF: http://proceedings.mlr.press/v5/hanneke09a/hanneke09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-hanneke09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Hanneke
given: Steve
- family: Xing
given: Eric P.
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 209-215
id: hanneke09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 209
lastpage: 215
published: 2009-04-15 00:00:00 +0000
- title: 'Distilled sensing: selective sampling for sparse signal recovery'
abstract: 'A selective sampling methodology called Distilled Sensing (DS) is proposed for recovering sparse signals in noise. DS exploits the fact that it is often easier to rule out locations that do not contain signal than it is to detect the locations of non-zero signal components. We formalize this observation and use it to devise a sequential selective sensing strategy that focuses sensing/measurement resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery compared to non-adaptive sensing. We show that exponentially weaker sparse signals can be recovered via DS compared with conventional non-adaptive sensing.'
volume: 5
URL: http://proceedings.mlr.press/v5/haupt09a.html
PDF: http://proceedings.mlr.press/v5/haupt09a/haupt09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-haupt09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Haupt
given: Jarvis
- family: Castro
given: Rui
- family: Nowak
given: Robert
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 216-223
id: haupt09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 216
lastpage: 223
published: 2009-04-15 00:00:00 +0000
- title: 'Infinite Hierarchical Hidden Markov Models'
abstract: 'In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowing a potentially unbounded number of levels in the hierarchy, instead of requiring the specification of a fixed hierarchy depth. Inference and learning are performed efficiently using Gibbs sampling and a modified forward-backtrack algorithm. We show encouraging demonstrations of the workings of the IHHMM.'
volume: 5
URL: http://proceedings.mlr.press/v5/heller09a.html
PDF: http://proceedings.mlr.press/v5/heller09a/heller09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-heller09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Heller
given: Katherine
- family: Teh
given: Yee Whye
- family: Gorur
given: Dilan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 224-231
id: heller09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 224
lastpage: 231
published: 2009-04-15 00:00:00 +0000
- title: 'An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward'
abstract: 'We derive a new expectation maximization algorithm for policy optimization in linear Gaussian Markov decision processes, where the reward function is parameterised in terms of a flexible mixture of Gaussians. This approach exploits both analytical tractability and numerical optimization. Consequently, on the one hand, it is more flexible and general than closed-form solutions, such as the widely used linear quadratic Gaussian (LQG) controllers. On the other hand, it is more accurate and faster than optimization methods that rely on approximation and simulation. Partial analytical solutions (though costly) eliminate the need for simulation and, hence, avoid approximation error. The experiments will show that for the same cost of computation, policy optimization methods that rely on analytical tractability have higher value than the ones that rely on simulation.'
volume: 5
URL: http://proceedings.mlr.press/v5/hoffman09a.html
PDF: http://proceedings.mlr.press/v5/hoffman09a/hoffman09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-hoffman09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Hoffman
given: Matthew
- family: Freitas
given: Nando
- family: Doucet
given: Arnaud
- family: Peters
given: Jan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 232-239
id: hoffman09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 232
lastpage: 239
published: 2009-04-15 00:00:00 +0000
- title: 'Maximum Entropy Density Estimation with Incomplete Presence-Only Data'
abstract: 'We demonstrate a generalization of Maximum Entropy Density Estimation that elegantly handles incomplete presence-only data. We provide a formulation that is able to learn from known values of incomplete data without having to learn imputed values, which may be inaccurate. This saves the effort needed to perform accurate imputation while observing the principle of maximum entropy throughout the learning process. We provide analysis and examples of our algorithm under different settings of missing data.'
volume: 5
URL: http://proceedings.mlr.press/v5/huang09a.html
PDF: http://proceedings.mlr.press/v5/huang09a/huang09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-huang09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Huang
given: Bert
- family: Salleb-Aouissi
given: Ansaf
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 240-247
id: huang09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 240
lastpage: 247
published: 2009-04-15 00:00:00 +0000
- title: 'Exploiting Probabilistic Independence for Permutations'
abstract: 'Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities. Recent Fourier-based approaches can be used to provide a compact representation over low-frequency components of the distribution. Though polynomial, the complexity of these representations grows very rapidly, especially if we want to maintain reasonable estimates for peaked distributions. In this paper, we first characterize the notion of probabilistic independence for distribution over permutations. We then present a method for factoring distributions into independent components in the Fourier domain, and use our algorithms to decompose large problems into much smaller ones. We demonstrate that our method provides very significant improvements in terms of running time, on real tracking data.'
volume: 5
URL: http://proceedings.mlr.press/v5/huang09b.html
PDF: http://proceedings.mlr.press/v5/huang09b/huang09b.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-huang09b.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Huang
given: Jonathan
- family: Guestrin
given: Carlos
- family: Jiang
given: Xiaoye
- family: Guibas
given: Leonidas
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 248-255
id: huang09b
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 248
lastpage: 255
published: 2009-04-15 00:00:00 +0000
- title: 'Particle Belief Propagation'
abstract: 'The popularity of particle filtering for inference in Markov chain models defined over random variables with very large or continuous domains makes it natural to consider sample–based versions of belief propagation (BP) for more general (tree–structured or loopy) graphs. Already, several such algorithms have been proposed in the literature. However, many questions remain open about the behavior of particle–based BP algorithms, and little theory has been developed to analyze their performance. In this paper, we describe a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods. We prove that this algorithm is consistent, approaching the true BP messages as the number of samples grows large. We then use concentration bounds to analyze the finite-sample behavior and give a convergence rate for the algorithm on tree–structured graphs. Our convergence rate is O(1/\sqrtn) where n is the number of samples, independent of the domain size of the variables.'
volume: 5
URL: http://proceedings.mlr.press/v5/ihler09a.html
PDF: http://proceedings.mlr.press/v5/ihler09a/ihler09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-ihler09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Ihler
given: Alexander
- family: McAllester
given: David
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 256-263
id: ihler09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 256
lastpage: 263
published: 2009-04-15 00:00:00 +0000
- title: 'Data Biased Robust Counter Strategies'
abstract: 'The problem of exploiting information about the environment while still being robust to inaccurate or incomplete information arises in many domains. Competitive imperfect information games where the goal is to maximally exploit an unknown opponent’s weaknesses are an example of this problem. Agents for these games must balance two objectives. First, they should aim to exploit data from past interactions with the opponent, seeking a best-response counter strategy. Second, they should aim to minimize losses since the limited data may be misleading or the opponent’s strategy may have changed, suggesting an opponent-agnostic Nash equilibrium strategy. In this paper, we show how to partially satisfy both of these objectives at the same time, producing strategies with favorable tradeoffs between the ability to exploit an opponent and the capacity to be exploited. Like a recently published technique, our approach involves solving a modified game; however the result is more generally applicable and even performs well in situations with very limited data. We evaluate our technique in the game of two-player, Limit Texas Hold’em.'
volume: 5
URL: http://proceedings.mlr.press/v5/johanson09a.html
PDF: http://proceedings.mlr.press/v5/johanson09a/johanson09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-johanson09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Johanson
given: Michael
- family: Bowling
given: Michael
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 264-271
id: johanson09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 264
lastpage: 271
published: 2009-04-15 00:00:00 +0000
- title: 'Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards'
abstract: 'We consider algorithms for selecting actions in order to maximize rewards chosen by an adversary, where the set of actions available on any given round is selected stochastically. We present the first polynomial-time no-regret algorithms for this setting. In the full-observation (experts) version of the problem, we present an exponential-weights algorithm that achieves regret O(\sqrtT log n), which is the best possible. For the bandit setting (where the algorithm only observes the reward of the action selected), we present a no-regret algorithm based on follow-the-perturbed-leader. This algorithm runs in polynomial time, unlike the EXP4 algorithm which can also be applied to this setting. Our algorithm has the interesting interpretation of solving a geometric experts problem where the embedding in which rewards are linear is never explicitly constructed. We argue that this adversarial-reward, stochastic availability formulation is important in practice, as assuming stationary stochastic rewards is unrealistic in many domains.'
volume: 5
URL: http://proceedings.mlr.press/v5/kanade09a.html
PDF: http://proceedings.mlr.press/v5/kanade09a/kanade09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-kanade09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Kanade
given: Varun
- family: McMahan
given: H. Brendan
- family: Bryan
given: Brent
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 272-279
id: kanade09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 272
lastpage: 279
published: 2009-04-15 00:00:00 +0000
- title: 'Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings'
abstract: 'We consider the task of dimensionality reduction for regression (DRR) informed by real-valued multivariate labels. The problem is often treated as a regression task where the goal is to find a low dimensional representation of the input data that preserves the statistical correlation with the targets. Recently, Covariance Operator Inverse Regression (COIR) was proposed as an effective solution that exploits the covariance structures of both input and output. COIR addresses known limitations of recent DRR techniques and allows a closed-form solution without resorting to explicit output space slicing often required by existing IR-based methods. In this work we provide a unifying view of COIR and other DRR techniques and relate them to the popular supervised dimensionality reduction methods including the canonical correlation analysis (CCA) and the linear discriminant analysis (LDA). We then show that COIR can be effectively extended to a semi-supervised learning setting where many of the input points lack their corresponding multivariate targets. A study of benefits of proposed approaches is presented on several important regression problems in both fully-supervised and semi-supervised settings.'
volume: 5
URL: http://proceedings.mlr.press/v5/kim09a.html
PDF: http://proceedings.mlr.press/v5/kim09a/kim09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-kim09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Kim
given: Minyoung
- family: Pavlovic
given: Vladimir
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 280-287
id: kim09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 280
lastpage: 287
published: 2009-04-15 00:00:00 +0000
- title: 'Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression'
abstract: 'The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model selection or confidence intervals for more detailed analysis requires cubic runtime, and thus constitutes a computational bottleneck in real-world data analysis. We propose a novel algorithm for KPLS which not only computes (a) the fit, but also (b) its approximate degrees of freedom and (c) error bars in quadratic runtime. The algorithm exploits a close connection between Kernel PLS and the Lanczos algorithm for approximating the eigenvalues of symmetric matrices, and uses this approximation to compute the trace of powers of the kernel matrix in quadratic runtime.'
volume: 5
URL: http://proceedings.mlr.press/v5/kramer09a.html
PDF: http://proceedings.mlr.press/v5/kramer09a/kramer09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-kramer09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Kramer
given: Nicole
- family: Sugiyama
given: Masashi
- family: Braun
given: Mikio
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 288-295
id: kramer09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 288
lastpage: 295
published: 2009-04-15 00:00:00 +0000
- title: 'Convex Perturbations for Scalable Semidefinite Programming'
abstract: 'Many important machine learning problems are modeled and solved via semidefinite programs; examples include metric learning, nonlinear embedding, and certain clustering problems. Often, off-the-shelf software is invoked for the associated optimization, which can be inappropriate due to excessive computational and storage requirements. In this paper, we introduce the use of convex perturbations for solving semidefinite programs (SDPs), and for a specific perturbation we derive an algorithm that has several advantages over existing techniques: a) it is simple, requiring only a few lines of Matlab, b) it is a first-order method, and thereby scalable, and c) it can easily exploit the structure of a given SDP (e.g., when the constraint matrices are low-rank, a situation common to several machine learning SDPs). A pleasant byproduct of our method is a fast, kernelized version of the large-margin nearest neighbor metric learning algorithm. We demonstrate that our algorithm is effective in finding fast approximations to large-scale SDPs arising in some machine learning applications.'
volume: 5
URL: http://proceedings.mlr.press/v5/kulis09a.html
PDF: http://proceedings.mlr.press/v5/kulis09a/kulis09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-kulis09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Kulis
given: Brian
- family: Sra
given: Suvrit
- family: Dhillon
given: Inderjit
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 296-303
id: kulis09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 296
lastpage: 303
published: 2009-04-15 00:00:00 +0000
- title: 'Sampling Techniques for the Nystrom Method'
abstract: 'The Nystrom method is an efficient technique to generate low-rank matrix approximations and is used in several large-scale learning applications. A key aspect of this method is the distribution according to which columns are sampled from the original matrix. In this work, we present an analysis of different sampling techniques for the Nystrom method. Our analysis includes both empirical and theoretical components. We first present novel experiments with several real world datasets, comparing the performance of the Nystrom method when used with uniform versus non-uniform sampling distributions. Our results suggest that uniform sampling without replacement, in addition to being more efficient both in time and space, produces more effective approximations. This motivates the theoretical part of our analysis which gives the first performance bounds for the Nystrom method precisely when used with uniform sampling without replacement.'
volume: 5
URL: http://proceedings.mlr.press/v5/kumar09a.html
PDF: http://proceedings.mlr.press/v5/kumar09a/kumar09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-kumar09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Kumar
given: Sanjiv
- family: Mohri
given: Mehryar
- family: Talwalkar
given: Ameet
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 304-311
id: kumar09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 304
lastpage: 311
published: 2009-04-15 00:00:00 +0000
- title: 'Deep Learning using Robust Interdependent Codes'
abstract: 'We investigate a simple yet effective method to introduce inhibitory and excitatory interactions between units in the layers of a deep neural network classifier. The method is based on the greedy layer-wise procedure of deep learning algorithms and extends the denoising autoencoder of Vincent et al. \citeVincentPLarochelleH2008-small by adding asymmetric lateral connections between its hidden coding units, in a manner that is much simpler and computationally more efficient than previously proposed approaches.We present experiments on two character recognition problems which show for the first time that lateral connections can significantly improve the classification performance of deep networks.'
volume: 5
URL: http://proceedings.mlr.press/v5/larochelle09a.html
PDF: http://proceedings.mlr.press/v5/larochelle09a/larochelle09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-larochelle09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Larochelle
given: Hugo
- family: Erhan
given: Dumitru
- family: Vincent
given: Pascal
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 312-319
id: larochelle09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 312
lastpage: 319
published: 2009-04-15 00:00:00 +0000
- title: 'Group Nonnegative Matrix Factorization for EEG Classification'
abstract: 'Given EEG data measured from several subjects under the same condition, our goal is to estimate common task-related bases in a linear model that capture intra-subject variations as well as inter-subject variations. Such bases capture the common phenomenon in a group data, which is known as group analysis. In this paper we present a method of nonnegative matrix factorization (NMF) that is well suited to analyze EEG data of multiple subjects. The method is referred to as group nonnegative matrix factorization (GNMF) where we seek task-related common bases reflecting both intra-subject and inter-subject variations, as well as bases involving individual characteristics. We compare GNMF with NMF and some modified NMFs, in a task of learning spectral features from EEG data. Experiments on BCI competition data indicate that GNMF improves the EEG classification performance. In addition, we also show that GNMF is useful in a task of subject-to-subject transfer where the prediction for an unseen subject is performed based on a linear model learned from different subjects in the same group.'
volume: 5
URL: http://proceedings.mlr.press/v5/lee09a.html
PDF: http://proceedings.mlr.press/v5/lee09a/lee09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-lee09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Lee
given: Hyekyoung
- family: Choi
given: Seungjin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 320-327
id: lee09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 320
lastpage: 327
published: 2009-04-15 00:00:00 +0000
- title: 'Kernel Learning by Unconstrained Optimization'
abstract: 'We study the problem of learning a kernel matrix from an apriori kernel and training data. In this context, we propose a new unconstrained convex optimization formulation, with an arbitrary convex second-order differentiable loss function on kernel entries and a LogDet divergence term for regularization. Since the number of variables is of order $O(n^2)$, the computational cost of standard Newton and quasi-Newton methods for learning a kernel matrix is prohibitive. Here an operator form Hessian is used to develop an $O(n^3)$ trust-region inexact Newton method, where the Newton direction is computed using several conjugate gradient steps on the Hessian operator equation. On the uspst dataset, our algorithm can handle 2 million optimization variables within one hour. Experiments are shown for both linear (Mahalanobis) metric learning and for kernel learning. The convergence rate, speed and performance of several loss functions and algorithms are discussed.'
volume: 5
URL: http://proceedings.mlr.press/v5/li09a.html
PDF: http://proceedings.mlr.press/v5/li09a/li09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-li09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Li
given: Fuxin
- family: Fu
given: Yunshan
- family: Dai
given: Yu-Hong
- family: Sminchisescu
given: Cristian
- family: Wang
given: Jue
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 328-335
id: li09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 328
lastpage: 335
published: 2009-04-15 00:00:00 +0000
- title: 'Latent Wishart Processes for Relational Kernel Learning'
abstract: 'In this paper, we propose a novel relational kernel learning model based on latent Wishart processes (LWP) to learn the kernel function for relational data. This is done by seamlessly integrating the relational information and the input attributes into the kernel learning process. Through extensive experiments on diverse real-world applications, we demonstrate that our LWP model can give very promising performance in practice.'
volume: 5
URL: http://proceedings.mlr.press/v5/li09b.html
PDF: http://proceedings.mlr.press/v5/li09b/li09b.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-li09b.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Li
given: Wu-Jun
- family: Zhang
given: Zhihua
- family: Yeung
given: Dit-Yan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 336-343
id: li09b
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 336
lastpage: 343
published: 2009-04-15 00:00:00 +0000
- title: 'Tighter and Convex Maximum Margin Clustering'
abstract: 'Maximum margin principle has been successfully applied to many supervised and semi-supervised problems in machine learning. Recently, this principle was extended for clustering, referred to as Maximum Margin Clustering (MMC) and achieved promising performance in recent studies. To avoid the problem of local minima, MMC can be solved globally via convex semi-definite programming (SDP) relaxation. Although many efficient approaches have been proposed to alleviate the computational burden of SDP, convex MMCs are still not scalable for medium data sets. In this paper, we propose a novel convex optimization method, LG-MMC, which maximizes the margin of opposite clusters via label generation. It can be shown that LG-MMC is much more scalable than existing convex approaches. Moreover, we show that our convex relaxation is tighter than state-of-art convex MMCs. Experiments on eighteen UCI datasets and MNIST dataset show significant improvement over existing MMC algorithms.'
volume: 5
URL: http://proceedings.mlr.press/v5/li09c.html
PDF: http://proceedings.mlr.press/v5/li09c/li09c.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-li09c.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Li
given: Yu-Feng
- family: Tsang
given: Ivor W.
- family: Kwok
given: Jame
- family: Zhou
given: Zhi-Hua
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 344-351
id: li09c
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 344
lastpage: 351
published: 2009-04-15 00:00:00 +0000
- title: 'Learning Exercise Policies for American Options'
abstract: 'Options are important instruments in modern finance. In this paper, we investigate reinforcement learning (RL) methods—in particular, least-squares policy iteration (LSPI)—for the problem of learning exercise policies for American options. We develop finite-time bounds on the performance of the policy obtained with LSPI and compare LSPI and the fitted Q-iteration algorithm (FQI) with the Longstaff-Schwartz method (LSM), the standard least-squares Monte Carlo algorithm from the finance community. Our empirical results show that the exercise policies discovered by LSPI and FQI gain larger payoffs than those discovered by LSM, on both real and synthetic data. Furthermore, we find that for all methods the policies learned from real data generally gain similar payoffs to the policies learned from simulated data. Our work shows that solution methods developed in machine learning can advance the state-of-the-art in an important and challenging application area, while demonstrating that computational finance remains a promising area for future applications of machine learning methods.'
volume: 5
URL: http://proceedings.mlr.press/v5/li09d.html
PDF: http://proceedings.mlr.press/v5/li09d/li09d.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-li09d.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Li
given: Yuxi
- family: Szepesvari
given: Csaba
- family: Schuurmans
given: Dale
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 352-359
id: li09d
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 352
lastpage: 359
published: 2009-04-15 00:00:00 +0000
- title: 'Learning Sparse Markov Network Structure via Ensemble-of-Trees Models'
abstract: 'Learning the sparse structure of a general Markov network is a hard problem. One of the main difficulties is the computation of its generally intractable partition function. To circumvent this difficulty, this paper proposes to learn the network structure using an ensemble-of-trees (ET) model. The ET model was first introduced by Meila and Jaakkola [1], and it approximates a Markov network using a mixture of all possible (super-exponentially many) spanning trees. The advantage of the ET model is that, although it needs to sum over super-exponentially many trees, its partition function as well as data likelihood can be computed in a closed form. Furthermore, since the ET model tends to represent a Markov network using as small number of trees as possible, it provides a natural regularization for finding a sparse network structure. Our simulation results show that the proposed ET approach is able to accurately recover the true Markov network connectivity and significantly outperform the state-of-art approaches for both discrete and continuous random variable networks. Furthermore, we also demonstrate the usage of the ET model for discovering the network of words from blog posts.'
volume: 5
URL: http://proceedings.mlr.press/v5/lin09a.html
PDF: http://proceedings.mlr.press/v5/lin09a/lin09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-lin09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Lin
given: Yuanqing
- family: Zhu
given: Shenghuo
- family: Lee
given: Daniel
- family: Taskar
given: Ben
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 360-367
id: lin09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 360
lastpage: 367
published: 2009-04-15 00:00:00 +0000
- title: 'A kernel method for unsupervised structured network inference'
abstract: 'Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernel-based approaches to this problem share a set of common features: (i) they are supervised and hence require labeled training data; (ii) edges in the network are treated as mutually independent and hence topological properties are largely ignored; (iii) they lack a statistical interpretation. We argue that these common assumptions are often undesirable for network inference, and propose (i) an unsupervised kernel method (ii) that takes the global structure of the network into account and (iii) is statistically motivated. We show that our approach can explain commonly used heuristics in statistical terms. In experiments on social networks, different variants of our method demonstrate appealing predictive performance.'
volume: 5
URL: http://proceedings.mlr.press/v5/lippert09a.html
PDF: http://proceedings.mlr.press/v5/lippert09a/lippert09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-lippert09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Lippert
given: Christoph
- family: Stegle
given: Oliver
- family: Ghahramani
given: Zoubin
- family: Borgwardt
given: Karsten
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 368-375
id: lippert09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 368
lastpage: 375
published: 2009-04-15 00:00:00 +0000
- title: 'Estimation Consistency of the Group Lasso and its Applications'
abstract: 'We extend the \ell_2-consistency result of (Meinshausen and Yu 2008) from the Lasso to the group Lasso. Our main theorem shows that the group Lasso achieves estimation consistency under a mild condition and an asymptotic upper bound on the number of selected variables can be obtained. As a result, we can apply the nonnegative garrote procedure to the group Lasso result to obtain an estimator which is simultaneously estimation and variable selection consistent. In particular, our setting allows both the number of groups and the number of variables per group increase and thus is applicable to high-dimensional problems. We also provide estimation consistency analysis for a version of the sparse additive models with increasing dimensions. Some finite-sample results are also reported.'
volume: 5
URL: http://proceedings.mlr.press/v5/liu09a.html
PDF: http://proceedings.mlr.press/v5/liu09a/liu09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-liu09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Liu
given: Han
- family: Zhang
given: Jian
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 376-383
id: liu09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 376
lastpage: 383
published: 2009-04-15 00:00:00 +0000
- title: 'Learning a Parametric Embedding by Preserving Local Structure'
abstract: 'The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on two datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.'
volume: 5
URL: http://proceedings.mlr.press/v5/maaten09a.html
PDF: http://proceedings.mlr.press/v5/maaten09a/maaten09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-maaten09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Maaten
given: Laurens
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 384-391
id: maaten09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 384
lastpage: 391
published: 2009-04-15 00:00:00 +0000
- title: 'Tractable Search for Learning Exponential Models of Rankings'
abstract: 'We consider the problem of learning the Generalized Mallows (GM) model of \citefv86, which represents a probability distribution over all possible permutations (aka rankings) of a given set of objects. The training data consists of a set of permutations. This problem generalizes the well known rank aggregation problem. Maximum Likelihood estimation of the GM model is NP-hard. An exact but inefficient search-based method was recently proposed for this problem. Here we introduce the first non-trivial heuristic function for this search. We justify it theoretically, and show why it is admissible in practice. We experimentally demonstrate its effectiveness, and show that it is superior to existing techniques for learning the GM model. We also show good performance of a family of faster approximate methods of search.'
volume: 5
URL: http://proceedings.mlr.press/v5/mandhani09a.html
PDF: http://proceedings.mlr.press/v5/mandhani09a/mandhani09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-mandhani09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Mandhani
given: Bhushan
- family: Meila
given: Marina
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 392-399
id: mandhani09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 392
lastpage: 399
published: 2009-04-15 00:00:00 +0000
- title: 'Exact and Approximate Sampling by Systematic Stochastic Search'
abstract: 'We introduce _adaptive sequential rejection sampling_, an algorithm for generating exact samples from high-dimensional, discrete distributions, building on ideas from classical AI search. Just as systematic search algorithms like A* recursively build complete solutions from partial solutions, sequential rejection sampling recursively builds exact samples over high-dimensional spaces from exact samples over lower-dimensional subspaces. Our algorithm recovers widely-used particle filters as an approximate variant without adaptation, and a randomized version of the directed arc consistency algorithm with backtracking when applied to deterministic problems. In this paper, we present the mathematical and algorithmic underpinnings of our approach and measure its behavior on ferromagnetic Isings and other probabilistic graphical models, obtaining exact and approximate samples in a range of situations.'
volume: 5
URL: http://proceedings.mlr.press/v5/mansinghka09a.html
PDF: http://proceedings.mlr.press/v5/mansinghka09a/mansinghka09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-mansinghka09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Mansinghka
given: Vikash
- family: Roy
given: Daniel
- family: Jonas
given: Eric
- family: Tenenbaum
given: Joshua
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 400-407
id: mansinghka09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 400
lastpage: 407
published: 2009-04-15 00:00:00 +0000
- title: 'Spanning Tree Approximations for Conditional Random Fields'
abstract: 'In this work we show that one can train Conditional Random Fields of intractable graphs effectively and efficiently by considering a mixture of random spanning trees of the underlying graphical model. Furthermore, we show how a maximum-likelihood estimator of such a training objective can subsequently be used for prediction on the full graph. We present experimental results which improve on the state-of-the-art. Additionally, the training objective is less sensitive to the regularization than pseudo-likelihood based training approaches. We perform the experimental validation on two classes of data sets where structure is important: image denoising and multilabel classification.'
volume: 5
URL: http://proceedings.mlr.press/v5/pletscher09a.html
PDF: http://proceedings.mlr.press/v5/pletscher09a/pletscher09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-pletscher09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Pletscher
given: Patrick
- family: Ong
given: Cheng Soon
- family: Buhmann
given: Joachim
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 408-415
id: pletscher09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 408
lastpage: 415
published: 2009-04-15 00:00:00 +0000
- title: 'Chromatic PAC-Bayes Bounds for Non-IID Data'
abstract: 'Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned with IID data, and it is particularly so for margin classifiers. However, there are many practical cases where the training data show some dependencies and where the traditional IID assumption does not apply. Stating generalization bounds for such frameworks is therefore of the utmost interest, both from theoretical and practical standpoints. In this work, we propose the first – to the best of our knowledge – \pac-Bayes generalization bounds for classifiers trained on data exhibiting dependencies. The approach undertaken to establish our results is based on the decomposition of a so-called dependency graph that encodes the dependencies within the data, in sets of independent data, through the tool of graph fractional covers. Our bounds are very general, since being able to find an upper bound on the (fractional) chromatic number of the dependency graph is sufficient to get new \pac-Bayes bounds for specific settings. We show how our results can be used to derive bounds for bipartite ranking and windowed prediction on sequential data.'
volume: 5
URL: http://proceedings.mlr.press/v5/ralaivola09a.html
PDF: http://proceedings.mlr.press/v5/ralaivola09a/ralaivola09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-ralaivola09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Ralaivola
given: Liva
- family: Szafranski
given: Marie
- family: Stempfel
given: Guillaume
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 416-423
id: ralaivola09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 416
lastpage: 423
published: 2009-04-15 00:00:00 +0000
- title: 'Inverse Optimal Heuristic Control for Imitation Learning'
abstract: 'Imitation learning is an increasingly important tool for both developing automatic decision making systems as well as for learning to predict decision-making and behavior by observation. Two basic approaches are common: the first, which we here term \emphbehavioral cloning (BC)\citeBehavioralCloning,ALVINN,DAVE, treats the imitation learning problem as a straightforward one of supervised learning (e.g. classification) where the goal is to map observations to controls. Secondly, the notion of\emphinverse optimal control (IOC) \citeBoydIOC,ng00irl,Abbeel04c,mmp06 for modeling such decision making behavior has gained prominence as it allows for learned decision-making that reasons sequentially and over a long horizon. Unfortunately, such inverse optimal control methods rely upon the ability to efficiently solve a planning problem and suffer from the usual “curse of dimensionality” when the state space gets large. This paper presents a novel approach to imitation learning that we call \emphInverse Optimal Heuristic Control (IOHC) which capitalizes on the strengths of both paradigms by allowing long-horizon, planning style reasoning in a low dimensional space, while enabling a high dimensional additional set of features to guide overall action selection. We frame this combined problem as one of optimization, and although the resulting objective function is actually non-convex, we are able to provide convex upper and lower bounds to optimize as surrogates. Further, these bounds, as well as our empirical results, show that the objective function is nearly convex and leads to improved performance on a set of imitation learning problems including turn prediction of drivers as well as predicting the likely paths taken by pedestrians in an office environment.'
volume: 5
URL: http://proceedings.mlr.press/v5/ratliff09a.html
PDF: http://proceedings.mlr.press/v5/ratliff09a/ratliff09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-ratliff09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Ratliff
given: Nathan
- family: Ziebart
given: Brian
- family: Peterson
given: Kevin
- family: Bagnell
given: J. Andrew
- family: Hebert
given: Martial
- family: Dey
given: Anind K.
- family: Srinivasa
given: Siddhartha
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 424-431
id: ratliff09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 424
lastpage: 431
published: 2009-04-15 00:00:00 +0000
- title: 'Learning the Switching Rate by Discretising Bernoulli Sources Online'
abstract: 'The expert tracking algorithm Fixed-Share depends on a parameter alpha, called the switching rate. If the final number of outcomes T is known in advance, then the switching rate can be learned with regret 1/2 log T + O(1) bits. The current fastest method that achieves this, Learn-alpha, is based on optimal discretisation of the Bernoulli distributions into O(√T) bins and runs in O(T√T) time; however the exact locations of these points have to be determined algorithmically. This paper introduces a new discretisation scheme with the same regret bound for known T, that specifies the number and positions of the discretisation points explicitly. The scheme is especially useful when T is not known in advance: a new fully on-line algorithm, Refine-Online, is presented, which runs in O(T√T log T) time and achieves a regret of 1/2 log 3 log T + O(log log T) bits.'
volume: 5
URL: http://proceedings.mlr.press/v5/rooij09a.html
PDF: http://proceedings.mlr.press/v5/rooij09a/rooij09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-rooij09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Rooij
given: Steven
- family: Erven
given: Tim
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 432-439
id: rooij09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 432
lastpage: 439
published: 2009-04-15 00:00:00 +0000
- title: 'Sequential Learning of Classifiers for Structured Prediction Problems'
abstract: 'Many classification problems with structured outputs can be regarded as a set of interrelated sub-problems where constraints dictate valid variable assignments. The standard approaches to these problems include either independent learning of individual classifiers for each of the sub-problems or joint learning of the entire set of classifiers with the constraints enforced during learning. We propose an intermediate approach where we learn these classifiers in a sequence using previously learned classifiers to guide learning of the next classifier by enforcing constraints between their outputs. We provide a theoretical motivation to explain why this learning protocol is expected to outperform both alternatives when individual problems have different ‘complexity’. This analysis motivates an algorithm for choosing a preferred order of classifier learning. We evaluate our technique on artificial experiments and on the entity and relation identification problem where the proposed method outperforms both joint and independent learning.'
volume: 5
URL: http://proceedings.mlr.press/v5/roth09a.html
PDF: http://proceedings.mlr.press/v5/roth09a/roth09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-roth09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Roth
given: Dan
- family: Small
given: Kevin
- family: Titov
given: Ivan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 440-447
id: roth09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 440
lastpage: 447
published: 2009-04-15 00:00:00 +0000
- title: 'Deep Boltzmann Machines'
abstract: 'We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and data-independent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer “pre-training” phase that allows variational inference to be initialized by a single bottom-up pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks.'
volume: 5
URL: http://proceedings.mlr.press/v5/salakhutdinov09a.html
PDF: http://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-salakhutdinov09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Salakhutdinov
given: Ruslan
- family: Hinton
given: Geoffrey
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 448-455
id: salakhutdinov09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 448
lastpage: 455
published: 2009-04-15 00:00:00 +0000
- title: 'Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm'
abstract: 'An optimization algorithm for minimizing a smooth function over a convex set is described. Each iteration of the method computes a descent direction by minimizing, over the original constraints, a diagonal plus low-rank quadratic approximation to the function. The quadratic approximation is constructed using a limited-memory quasi-Newton update. The method is suitable for large-scale problems where evaluation of the function is substantially more expensive than projection onto the constraint set. Numerical experiments on one-norm regularized test problems indicate that the proposed method is competitive with state-of-the-art methods such as bound-constrained L-BFGS and orthant-wise descent. We further show that the method generalizes to a wide class of problems, and substantially improves on state-of-the-art methods for problems such as learning the structure of Gaussian graphical models and Markov random fields.'
volume: 5
URL: http://proceedings.mlr.press/v5/schmidt09a.html
PDF: http://proceedings.mlr.press/v5/schmidt09a/schmidt09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-schmidt09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Schmidt
given: Mark
- family: Berg
given: Ewout
- family: Friedlander
given: Michael
- family: Murphy
given: Kevin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 456-463
id: schmidt09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 456
lastpage: 463
published: 2009-04-15 00:00:00 +0000
- title: 'Novelty detection: Unlabeled data definitely help'
abstract: 'In machine learning, one formulation of the novelty detection problem is to build a detector based on a training sample consisting of only nominal data. The standard (inductive) approach to this problem has been to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning time. We argue that novelty detection is naturally solved by a general reduction to a binary classification problem. In particular, a detector with a desired false positive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the inductive approach, our approach yields detectors that are optimal (e.g., statistically consistent) regardless of the distribution on novelties. Therefore, in novelty detection, unlabeled data have a substantial impact on the theoretical properties of the decision rule.'
volume: 5
URL: http://proceedings.mlr.press/v5/scott09a.html
PDF: http://proceedings.mlr.press/v5/scott09a/scott09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-scott09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Scott
given: Clayton
- family: Blanchard
given: Gilles
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 464-471
id: scott09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 464
lastpage: 471
published: 2009-04-15 00:00:00 +0000
- title: 'PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering'
abstract: 'We derive a PAC-Bayesian generalization bound for density estimation. Similar to the PAC-Bayesian generalization bound for classification, the result has the appealingly simple form of a tradeoff between empirical performance and the KL-divergence of the posterior from the prior. Moreover, the PAC-Bayesian generalization bound for classification can be derived as a special case of the bound for density estimation. To illustrate a possible application of our bound we derive a generalization bound for co-clustering. The bound provides a criterion to evaluate the ability of co-clustering to predict new co-occurrences, thus introducing a supervised flavor to this traditionally unsupervised task.'
volume: 5
URL: http://proceedings.mlr.press/v5/seldin09a.html
PDF: http://proceedings.mlr.press/v5/seldin09a/seldin09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-seldin09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Seldin
given: Yevgeny
- family: Tishby
given: Naftali
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 472-479
id: seldin09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 472
lastpage: 479
published: 2009-04-15 00:00:00 +0000
- title: 'PAC-Bayes Analysis Of Maximum Entropy Classification'
abstract: 'We extend and apply the PAC-Bayes theorem to the analysis of maximum entropy learning by considering maximum entropy classification. The theory introduces a multiple sampling technique that controls an effective margin of the bound. We further develop a dual implementation of the convex optimisation that optimises the bound. This algorithm is tested on some simple datasets and the value of the bound compared with the test error.'
volume: 5
URL: http://proceedings.mlr.press/v5/shawe-taylor09a.html
PDF: http://proceedings.mlr.press/v5/shawe-taylor09a/shawe-taylor09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-shawe-taylor09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Shawe-Taylor
given: John
- family: Hardoon
given: David
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 480-487
id: shawe-taylor09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 480
lastpage: 487
published: 2009-04-15 00:00:00 +0000
- title: 'Efficient graphlet kernels for large graph comparison'
abstract: 'State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting \it graphlets, \ie subgraphs with k nodes where k ∈{ 3, 4, 5 }. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.'
volume: 5
URL: http://proceedings.mlr.press/v5/shervashidze09a.html
PDF: http://proceedings.mlr.press/v5/shervashidze09a/shervashidze09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-shervashidze09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Shervashidze
given: Nino
- family: Vishwanathan
given: SVN
- family: Petri
given: Tobias
- family: Mehlhorn
given: Kurt
- family: Borgwardt
given: Karsten
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 488-495
id: shervashidze09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 488
lastpage: 495
published: 2009-04-15 00:00:00 +0000
- title: 'Hash Kernels'
abstract: 'We propose hashing to facilitate efficient kernels. This generalizes previous work using sampling and we show a principled way to compute the kernel matrix for data streams and sparse feature spaces. Moreover, we give deviation bounds from the exact kernel matrix. This has applications to estimation on strings and graphs.'
volume: 5
URL: http://proceedings.mlr.press/v5/shi09a.html
PDF: http://proceedings.mlr.press/v5/shi09a/shi09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-shi09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Shi
given: Qinfeng
- family: Petterson
given: James
- family: Dror
given: Gideon
- family: Langford
given: John
- family: Smola
given: Alex
- family: Strehl
given: Alex
- family: Vishwanathan
given: Vishy
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 496-503
id: shi09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 496
lastpage: 503
published: 2009-04-15 00:00:00 +0000
- title: 'Locally Minimax Optimal Predictive Modeling with Bayesian Networks'
abstract: 'We propose an information-theoretic approach for predictive modeling with Bayesian networks. Our approach is based on the minimax optimal Normalized Maximum Likelihood (NML) distribution, motivated by the MDL principle. In particular, we present a parameter learning method which, together with a previously introduced NML-based model selection criterion, provides a way to construct highly predictive Bayesian network models from data. The method is parameter-free and robust, unlike the currently popular Bayesian marginal likelihood approach which has been shown to be sensitive to the choice of prior hyperparameters. Empirical tests show that the proposed method compares favorably with the Bayesian approach in predictive tasks.'
volume: 5
URL: http://proceedings.mlr.press/v5/silander09a.html
PDF: http://proceedings.mlr.press/v5/silander09a/silander09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-silander09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Silander
given: Tomi
- family: Roos
given: Teemu
- family: Myllymaki
given: Petri
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 504-511
id: silander09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 504
lastpage: 511
published: 2009-04-15 00:00:00 +0000
- title: 'MCMC Methods for Bayesian Mixtures of Copulas'
abstract: 'Applications of copula models have been increasing in number in recent years. This class of models provides a modular parameterization of joint distributions: the specification of the marginal distributions is parameterized separately from the dependence structure of the joint, a convenient way of encoding a model for domains such as finance. Some recent advances on how to specify copulas for arbitrary dimensions have been proposed, by means of mixtures of decomposable graphical models. This paper introduces a Bayesian approach for dealing with mixtures of copulas which, due to the lack of prior conjugacy, raise computational challenges. We motivate and present families of Markov chain Monte Carlo (MCMC) proposals that exploit the particular structure of mixtures of copulas. Different algorithms are evaluated according to their mixing properties, and an application in financial forecasting with missing data illustrates the usefulness of the methodology.'
volume: 5
URL: http://proceedings.mlr.press/v5/silva09a.html
PDF: http://proceedings.mlr.press/v5/silva09a/silva09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-silva09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Silva
given: Ricardo
- family: Gramacy
given: Robert
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 512-519
id: silva09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 512
lastpage: 519
published: 2009-04-15 00:00:00 +0000
- title: 'Factorial Mixture of Gaussians and the Marginal Independence Model'
abstract: 'Marginal independence constraints play an important role in learning with graphical models. One way of parameterizing a model of marginal independencies is by building a latent variable model where two independent observed variables have no common latent source. In sparse domains, however, it might be advantageous to model the marginal observed distribution directly, without explicitly including latent variables in the model. There have been recent advances in Gaussian and binary models of marginal independence, but no models with non-linear dependencies between continuous variables has been proposed so far. In this paper, we describe how to generalize the Gaussian model of marginal independencies based on mixtures, and how to learn parameters. This requires a non-standard parameterization and raises difficult non-linear optimization issues.'
volume: 5
URL: http://proceedings.mlr.press/v5/silva09b.html
PDF: http://proceedings.mlr.press/v5/silva09b/silva09b.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-silva09b.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Silva
given: Ricardo
- family: Ghahramani
given: Zoubin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 520-527
id: silva09b
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 520
lastpage: 527
published: 2009-04-15 00:00:00 +0000
- title: 'Tractable Bayesian Inference of Time-Series Dependence Structure'
abstract: 'We consider the problem of Bayesian inference over graphical structures describing the interactions among multiple vector time-series. A directed temporal interaction model is presented which assumes a fixed dependence structure among time-series. Using a conjugate prior over this model’s structure and parameters, we focus our attention on characterizing the exact posterior uncertainty in the structure given data. The model is extended via the introduction of a dynamically evolving latent variable which indexes dependence structures over time. Performing inference using this model yields promising results when analyzing the interaction of multiple tracked moving objects.'
volume: 5
URL: http://proceedings.mlr.press/v5/siracusa09a.html
PDF: http://proceedings.mlr.press/v5/siracusa09a/siracusa09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-siracusa09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Siracusa
given: Michael
- family: III
given: John Fisher
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 528-535
id: siracusa09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 528
lastpage: 535
published: 2009-04-15 00:00:00 +0000
- title: 'Relative Novelty Detection'
abstract: 'Novelty detection is an important tool for unsupervised data analysis. It relies on finding regions of low density within which events are then flagged as novel. By design this is dependent on the underlying measure of the space. In this paper we derive a formulation which is able to address this problem by allowing for a reference measure to be given in the form of a sample from an alternate distribution. We show that this optimization problem can be solved efficiently and that it works well in practice.'
volume: 5
URL: http://proceedings.mlr.press/v5/smola09a.html
PDF: http://proceedings.mlr.press/v5/smola09a/smola09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-smola09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Smola
given: Alex
- family: Song
given: Le
- family: Teo
given: Choon Hui
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 536-543
id: smola09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 536
lastpage: 543
published: 2009-04-15 00:00:00 +0000
- title: 'Tree Block Coordinate Descent for MAP in Graphical Models'
abstract: 'A number of linear programming relaxations have been proposed for finding most likely settings of the variables (MAP) in large probabilistic models. The relaxations are often succinctly expressed in the dual and reduce to different types of reparameterizations of the original model. The dual objectives are typically solved by performing local block coordinate descent steps. In this work, we show how to perform block coordinate descent on spanning trees of the graphical model. We also show how all of the earlier dual algorithms are related to each other, giving transformations from one type of reparameterization to another while maintaining monotonicity relative to a common objective function. Finally, we quantify when the MAP solution can and cannot be decoded directly from the dual LP relaxation.'
volume: 5
URL: http://proceedings.mlr.press/v5/sontag09a.html
PDF: http://proceedings.mlr.press/v5/sontag09a/sontag09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-sontag09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Sontag
given: David
- family: Jaakkola
given: Tommi
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 544-551
id: sontag09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 544
lastpage: 551
published: 2009-04-15 00:00:00 +0000
- title: 'The Block Diagonal Infinite Hidden Markov Model'
abstract: 'The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states \citeihmm,hdp. We present a generalization of this framework that introduces block-diagonal structure in the transitions between the hidden states. These blocks correspond to “sub-behaviors” exhibited by data sequences. In identifying such structure, the model classifies, or partitions, sequence data according to these sub-behaviors in an unsupervised way. We present an application of this model to artificial data, a video gesture classification task, and a musical theme labeling task, and show that components of the model can also be applied to graph segmentation.'
volume: 5
URL: http://proceedings.mlr.press/v5/stepleton09a.html
PDF: http://proceedings.mlr.press/v5/stepleton09a/stepleton09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-stepleton09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Stepleton
given: Thomas
- family: Ghahramani
given: Zoubin
- family: Gordon
given: Geoffrey
- family: Lee
given: Tai-Sing
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 552-559
id: stepleton09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 552
lastpage: 559
published: 2009-04-15 00:00:00 +0000
- title: 'Variable Metric Stochastic Approximation Theory'
abstract: 'We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results in the areas of elicitation of properties of distributions using prediction markets and in learning from expert advice.'
volume: 5
URL: http://proceedings.mlr.press/v5/sunehag09a.html
PDF: http://proceedings.mlr.press/v5/sunehag09a/sunehag09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-sunehag09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Sunehag
given: Peter
- family: Trumpf
given: Jochen
- family: Vishwanathan
given: S.V.N.
- family: Schraudolph
given: Nicol
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 560-566
id: sunehag09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 560
lastpage: 566
published: 2009-04-15 00:00:00 +0000
- title: 'Variational Learning of Inducing Variables in Sparse Gaussian Processes'
abstract: 'Sparse Gaussian process methods that use inducing variables require the selection of the inducing inputs and the kernel hyperparameters. We introduce a variational formulation for sparse approximations that jointly infers the inducing inputs and the kernel hyperparameters by maximizing a lower bound of the true log marginal likelihood. The key property of this formulation is that the inducing inputs are defined to be variational parameters which are selected by minimizing the Kullback-Leibler divergence between the variational distribution and the exact posterior distribution over the latent function values. We apply this technique to regression and we compare it with other approaches in the literature.'
volume: 5
URL: http://proceedings.mlr.press/v5/titsias09a.html
PDF: http://proceedings.mlr.press/v5/titsias09a/titsias09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-titsias09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Titsias
given: Michalis
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 567-574
id: titsias09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 567
lastpage: 574
published: 2009-04-15 00:00:00 +0000
- title: 'Non-Negative Semi-Supervised Learning'
abstract: 'The contributions of this paper are three-fold. First, we present a general formulation for reaping the benefits from both non-negative data factorization and semi-supervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions, and greater discriminating power via extra unlabeled data. Then, an efficient multiplicative updating procedure is proposed along with its theoretic justification of the algorithmic convergency. Finally, the tensorization of this general formulation for non-negative semi-supervised learning is also briefed for handling tensor data of arbitrary order. Extensive experiments compared with the state-of-the-art algorithms for non-negative data factorization and semi-supervised learning demonstrate the algorithmic properties in sparsity, classification power, and robustness to image occlusions.'
volume: 5
URL: http://proceedings.mlr.press/v5/wang09a.html
PDF: http://proceedings.mlr.press/v5/wang09a/wang09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-wang09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Wang
given: Changhu
- family: Yan
given: Shuicheng
- family: Zhang
given: Lei
- family: Zhang
given: Hongjiang
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 575-582
id: wang09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 575
lastpage: 582
published: 2009-04-15 00:00:00 +0000
- title: 'Markov Topic Models'
abstract: 'We develop Markov topic models (MTMs), a novel family of generative graphical models that can learn topics simultaneously from multiple corpora, such as papers from different conferences. We apply Gaussian (Markov) random fields to model the correlations of different corpora. MTMs capture both the internal topic structure within each corpus and the relationships between topics across the corpora. We derive an efficient estimation procedure with variational expectation-maximization. We study the performance of our models on a corpus of abstracts from six different computer science conferences. Our analysis reveals qualitative discoveries that are not possible with traditional topic models, and improved quantitative performance over the state of the art.'
volume: 5
URL: http://proceedings.mlr.press/v5/wang09b.html
PDF: http://proceedings.mlr.press/v5/wang09b/wang09b.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-wang09b.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Wang
given: Chong
- family: Thiesson
given: Bo
- family: Meek
given: Chris
- family: Blei
given: David
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 583-590
id: wang09b
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 583
lastpage: 590
published: 2009-04-15 00:00:00 +0000
- title: 'An Information Geometry Approach for Distance Metric Learning'
abstract: 'Metric learning is an important problem in machine learning and pattern recognition. In this paper, we propose a framework for metric learning based on information geometry. The key idea is to construct two kernel matrices for the given training data: one is based on the distance metric and the other is based on the assigned class labels. Inspired by the idea of information geometry, we relate these two kernel matrices to two Gaussian distributions, and the difference between the two kernel matrices is then computed by the Kullback-Leibler (KL) divergence between the two Gaussian distributions. The optimal distance metric is then found by minimizing the divergence between the two distributions. Based on this idea, we present two metric learning algorithms, one for linear distance metric and the other for nonlinear distance with the introduction of a kernel function. Unlike many existing algorithms for metric learning that require solving a non-trivial optimization problem and therefore are computationally expensive when the data dimension is high, the proposed algorithms have a closed-form solution and are computationally more efficient. Extensive experiments with data classification and face recognition show that the proposed algorithms are comparable to or better than the state-of-the-art algorithms for metric learning.'
volume: 5
URL: http://proceedings.mlr.press/v5/wang09c.html
PDF: http://proceedings.mlr.press/v5/wang09c/wang09c.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-wang09c.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Wang
given: Shijun
- family: Jin
given: Rong
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 591-598
id: wang09c
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 591
lastpage: 598
published: 2009-04-15 00:00:00 +0000
- title: 'Large-Margin Structured Prediction via Linear Programming'
abstract: 'This paper presents a novel learning algorithm for structured classification problems, where the task is to predict multiple and interacting labels (multilabel) for an input object. The maximum margin separation between the correct multilabels and the incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of the column generation and the extragradient method for linear programming to gain further efficiency. Compared to previous works on large-margin structured prediction, this framework has advantages in handling arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of the proposed approach.'
volume: 5
URL: http://proceedings.mlr.press/v5/wang09d.html
PDF: http://proceedings.mlr.press/v5/wang09d/wang09d.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-wang09d.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Wang
given: Zhuoran
- family: Shawe-Taylor
given: John
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 599-606
id: wang09d
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 599
lastpage: 606
published: 2009-04-15 00:00:00 +0000
- title: 'A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation'
abstract: 'In this paper we present a doubly hierarchical Pitman-Yor process language model. Its bottom layer of hierarchy consists of multiple hierarchical Pitman-Yor process language models, one each for some number of domains. The novel top layer of hierarchy consists of a mechanism to couple together multiple language models such that they share statistical strength. Intuitively this sharing results in the ?adaptation? of a latent shared language model to each domain. We introduce a general formalism capable of describing the overall model which we call the graphical Pitman-Yor process and explain how to perform Bayesian inference in it. We present encouraging language model domain adaptation results that both illustrate the potential benefits of our new model and suggest new avenues of inquiry.'
volume: 5
URL: http://proceedings.mlr.press/v5/wood09a.html
PDF: http://proceedings.mlr.press/v5/wood09a/wood09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-wood09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Wood
given: Frank
- family: Teh
given: Yee Whye
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 607-614
id: wood09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 607
lastpage: 614
published: 2009-04-15 00:00:00 +0000
- title: 'Speed and Sparsity of Regularized Boosting'
abstract: 'Boosting algorithms with l1 regularization are of interest because l1 regularization leads to sparser composite classifiers. Moreover, Rosset et al. have shown that for separable data, standard lp regularized loss minimization results in a margin maximizing classifier in the limit as regularization is relaxed. For the case p=1, we extend these results by obtaining explicit convergence bounds on the regularization required to yield a margin within prescribed accuracy of the maximum achievable margin. We derive similar rates of convergence for the epsilon AdaBoost algorithm, in the process providing a new proof that epsilon AdaBoost is margin maximizing as epsilon converges to 0. Because both of these known algorithms are computationally expensive, we introduce a new hybrid algorithm, AdaBoost+L1, that combines the virtues of AdaBoost with the sparsity of l1 regularization in a computationally efficient fashion. We prove that the algorithm is margin maximizing and empirically examine its performance on five datasets.'
volume: 5
URL: http://proceedings.mlr.press/v5/xi09a.html
PDF: http://proceedings.mlr.press/v5/xi09a/xi09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-xi09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Xi
given: Yongxin
- family: Xiang
given: Zhen
- family: Ramadge
given: Peter
- family: Schapire
given: Robert
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 615-622
id: xi09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 615
lastpage: 622
published: 2009-04-15 00:00:00 +0000
- title: 'Tree-Based Inference for Dirichlet Process Mixtures'
abstract: 'The Dirichlet process mixture (DPM) is a widely used model for clustering and for general nonparametric Bayesian density estimation. Unfortunately, like in many statistical models, exact inference in a DPM is intractable, and approximate methods are needed to perform efficient inference. While most attention in the literature has been placed on Markov chain Monte Carlo (MCMC), variational Bayesian (VB) and collapsed variational methods, Heller and Ghahramani (2005) recently introduced a novel class of approximation for DPMs based on Bayesian hierarchical clustering (BHC). These tree-based combinatorial approximations efficiently sum over exponentially many ways of partitioning the data and offer a novel lower bound on the marginal likelihood of the DPM. In this paper we make the following contributions: (1) We show empirically that the BHC lower bounds are substantially tighter than the bounds given by VB and by collapsed variational methods on synthetic and real datasets. (2) We also show that BHC offers a more accurate predictive performance on these datasets. (3) We further improve the tree-based lower bounds with an algorithm that efficiently sums contributions from alternative trees. (4) We present a fast approximate method for BHC. Our results suggest that our combinatorial approximate inference methods and lower bounds may be useful not only in DPMs but in other models as well.'
volume: 5
URL: http://proceedings.mlr.press/v5/xu09a.html
PDF: http://proceedings.mlr.press/v5/xu09a/xu09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-xu09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Xu
given: Yang
- family: Heller
given: Katherine
- family: Ghahramani
given: Zoubin
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 623-630
id: xu09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 623
lastpage: 630
published: 2009-04-15 00:00:00 +0000
- title: 'Dual Temporal Difference Learning'
abstract: 'Recently, researchers have investigated novel dual representations as a basis for dynamic programming and reinforcement learning algorithms. Although the convergence properties of classical dynamic programming algorithms have been established for dual representations, temporal difference learning algorithms have not yet been analyzed. In this paper, we study the convergence properties of temporal difference learning using dual representations. We contribute significant progress by proving the convergence of dual temporal difference learning with eligibility traces. Experimental results suggest that the dual algorithms seem to demonstrate empirical benefits over standard primal algorithms.'
volume: 5
URL: http://proceedings.mlr.press/v5/yang09a.html
PDF: http://proceedings.mlr.press/v5/yang09a/yang09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-yang09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Yang
given: Min
- family: Li
given: Yuxi
- family: Schuurmans
given: Dale
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 631-638
id: yang09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 631
lastpage: 638
published: 2009-04-15 00:00:00 +0000
- title: 'Active Sensing'
abstract: 'Labels are often expensive to get, and this motivates \emphactive learning which chooses the most informative samples for label acquisition. In this paper we study \emphactive sensing in a multi-view setting, motivated from many problems where grouped features are also expensive to obtain and need to be acquired (or \emphsensed) actively (e.g., in cancer diagnosis each patient might go through many tests such as CT, Ultrasound and MRI to get valuable features). The strength of this model is that one actively sensed (sample, view) pair would improve the \emphjoint multi-view classification on all the samples. For this purpose we extend the Bayesian co-training framework such that it can handle missing views in a principled way, and introduce two criteria for view acquisition. Experiments on one toy data and two real-world medical problems show the effectiveness of this model.'
volume: 5
URL: http://proceedings.mlr.press/v5/yu09a.html
PDF: http://proceedings.mlr.press/v5/yu09a/yu09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-yu09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Yu
given: Shipeng
- family: Krishnapuram
given: Balaji
- family: Rosales
given: Romer
- family: Rao
given: R. Bharat
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 639-646
id: yu09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 639
lastpage: 646
published: 2009-04-15 00:00:00 +0000
- title: 'Coherence Functions for Multicategory Margin-based Classification Methods'
abstract: 'Margin-based classification methods are typically devised based on a majorization-minimization procedure, which approximately solves an otherwise intractable minimization problem defined with the 0-l loss. However, extension of such methods from the binary classification setting to the more general multicategory setting turns out to be non-trivial. In this paper, our focus is to devise margin-based classification methods that can be seamlessly applied to both settings, with the binary setting simply as a special case. In particular, we propose a new majorization loss function that we call the coherence function, and then devise a new multicategory margin-based boosting algorithm based on the coherence function. Analogous to deterministic annealing, the coherence function is characterized by a temperature factor. It is closely related to the multinomial log-likelihood function and its limit at zero temperature corresponds to a multicategory hinge loss function.'
volume: 5
URL: http://proceedings.mlr.press/v5/zhang09a.html
PDF: http://proceedings.mlr.press/v5/zhang09a/zhang09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-zhang09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Zhang
given: Zhihua
- family: Jordan
given: Michael
- family: Li
given: Wu-Jun
- family: Yeung
given: Dit-Yan
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 647-654
id: zhang09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 647
lastpage: 654
published: 2009-04-15 00:00:00 +0000
- title: 'Latent Variable Models for Dimensionality Reduction'
abstract: 'Principal coordinate analysis (PCO), as a duality of principal component analysis (PCA), is also a classical method for explanatory data analysis. In this paper we propose a probabilistic PCO by using a normal latent variable model in which maximum likelihood estimation and an expectation-maximization algorithm are respectively devised to calculate the configurations of objects in a low-dimensional Euclidean space. We also devise probabilistic formulations for kernel PCA which is a nonlinear extension of PCA.'
volume: 5
URL: http://proceedings.mlr.press/v5/zhang09b.html
PDF: http://proceedings.mlr.press/v5/zhang09b/zhang09b.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-zhang09b.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Zhang
given: Zhihua
- family: Jordan
given: Michael
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 655-662
id: zhang09b
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 655
lastpage: 662
published: 2009-04-15 00:00:00 +0000
- title: 'Reversible Jump MCMC for Non-Negative Matrix Factorization'
abstract: 'We present a fully Bayesian approach to Non-Negative Matrix Factorisation (NMF) by developing a Reversible Jump Markov Chain Monte Carlo (RJMCMC) method which provides full posteriors over the matrix components. In addition the NMF model selection issue is addressed, for the first time, as our RJMCMC procedure provides the posterior distribution over the matrix dimensions and therefore the number of components in the NMF model. A comparative analysis is provided with the Bayesian Information Criterion (BIC) and model selection employing estimates of the marginal likelihood. An illustrative synthetic example is provided using blind mixtures of images. This is then followed by a large scale study of the recovery of component spectra from multiplexed Raman readouts. The power and flexibility of the Bayesian methodology and the proposed RJMCMC procedure to objectively assess differing model structures and infer the corresponding plausible component spectra for this complex data is demonstrated convincingly.'
volume: 5
URL: http://proceedings.mlr.press/v5/zhong09a.html
PDF: http://proceedings.mlr.press/v5/zhong09a/zhong09a.pdf
edit: https://github.com/mlresearch/v5/edit/gh-pages/_posts/2009-04-15-zhong09a.md
series: 'Proceedings of Machine Learning Research'
container-title: 'Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics'
publisher: 'PMLR'
author:
- family: Zhong
given: Mingjun
- family: Girolami
given: Mark
editor:
- family: Dyk
given: David van
- family: Welling
given: Max
address: Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
page: 663-670
id: zhong09a
issued:
date-parts:
- 2009
- 4
- 15
firstpage: 663
lastpage: 670
published: 2009-04-15 00:00:00 +0000