Volume 31: Artificial Intelligence and Statistics, 29-1 May 2013, Scottsdale, Arizona, USA


Editors: Carlos M. Carvalho, Pradeep Ravikumar



Part I: Notable Papers

Bayesian learning of joint distributions of objects

Anjishnu Banerjee,  Jared Murray,  David Dunson ; PMLR 31:1-9

Permutation estimation and minimax rates of identifiability

Olivier Collier,  Arnak Dalalyan ; PMLR 31:10-19

A unifying representation for a class of dependent random measures

Nicholas Foti,  Joseph Futoma,  Daniel Rockmore,  Sinead Williamson ; PMLR 31:20-28

Diagonal Orthant Multinomial Probit Models

James Johndrow,  David Dunson,  Kristian Lum ; PMLR 31:29-38

Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods

Zhaoshi Meng,  Dennis Wei,  Ami Wiesel,  Alfred Hero III ; PMLR 31:39-47

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series

Zhaoran Wang,  Fang Han,  Han Liu ; PMLR 31:48-56

Part II: Regular Papers

A Competitive Test for Uniformity of Monotone Distributions

Jayadev Acharya,  Ashkan Jafarpour,  Alon Orlitsky,  Ananda Suresh ; PMLR 31:57-65

Clustering Oligarchies

Margareta Ackerman,  Shai Ben-David,  David Loker,  Sivan Sabato ; PMLR 31:66-74

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes

Andrej Aderhold,  Dirk Husmeier,  V. Anne Smith ; PMLR 31:75-84

Nystrom Approximation for Large-Scale Determinantal Processes

Raja Hafiz Affandi,  Alex Kulesza,  Emily Fox,  Ben Taskar ; PMLR 31:85-98

Further Optimal Regret Bounds for Thompson Sampling

Shipra Agrawal,  Navin Goyal ; PMLR 31:99-107

Distributed and Adaptive Darting Monte Carlo through Regenerations

Sungjin Ahn,  Yutian Chen,  Max Welling ; PMLR 31:108-116

Consensus Ranking with Signed Permutations

Raman Arora,  Marina Meila ; PMLR 31:117-125

Ultrahigh Dimensional Feature Screening via RKHS Embeddings

Krishnakumar Balasubramanian,  Bharath Sriperumbudur,  Guy Lebanon ; PMLR 31:126-134

Meta-Transportability of Causal Effects: A Formal Approach

Elias Bareinboim,  Judea Pearl ; PMLR 31:135-143

Convex Collective Matrix Factorization

Guillaume Bouchard,  Dawei Yin,  Shengbo Guo ; PMLR 31:144-152

Efficiently Sampling Probabilistic Programs via Program Analysis

Arun Chaganty,  Aditya Nori,  Sriram Rajamani ; PMLR 31:153-160

Computing the M Most Probable Modes of a Graphical Model

Chao Chen,  Vladimir Kolmogorov,  Yan Zhu,  Dimitris Metaxas,  Christoph Lampert ; PMLR 31:161-169

A simple criterion for controlling selection bias

Eunice Yuh-Jie Chen,  Judea Pearl ; PMLR 31:170-177

Evidence Estimation for Bayesian Partially Observed MRFs

Yutian Chen,  Max Welling ; PMLR 31:178-186

Why Steiner-tree type algorithms work for community detection

Mung Chiang,  Henry Lam,  Zhenming Liu,  Vincent Poor ; PMLR 31:187-195

A simple sketching algorithm for entropy estimation over streaming data

Peter Clifford,  Ioana Cosma ; PMLR 31:196-206

Deep Gaussian Processes

Andreas Damianou,  Neil Lawrence ; PMLR 31:207-215

ODE parameter inference using adaptive gradient matching with Gaussian processes

Frank Dondelinger,  Dirk Husmeier,  Simon Rogers,  Maurizio Filippone ; PMLR 31:216-228

Uncover Topic-Sensitive Information Diffusion Networks

Nan Du,  Le Song,  Hyenkyun Woo,  Hongyuan Zha ; PMLR 31:229-237

Stochastic blockmodeling of relational event dynamics

Christopher DuBois,  Carter Butts,  Padhraic Smyth ; PMLR 31:238-246

Dynamic Copula Networks for Modeling Real-valued Time Series

Elad Eban,  Gideon Rothschild,  Adi Mizrahi,  Israel Nelken,  Gal Elidan ; PMLR 31:247-255

Data-driven covariate selection for nonparametric estimation of causal effects

Doris Entner,  Patrik Hoyer,  Peter Spirtes ; PMLR 31:256-264

Learning to Top-K Search using Pairwise Comparisons

Brian Eriksson ; PMLR 31:265-273

Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension

Hamed Firouzi,  Bala Rajaratnam,  Alfred Hero III ; PMLR 31:274-288

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

Georg Goerg,  Cosma Shalizi ; PMLR 31:289-297

Unsupervised Link Selection in Networks

Quanquan Gu,  Charu Aggarwal,  Jiawei Han ; PMLR 31:298-306

Clustered Support Vector Machines

Quanquan Gu,  Jiawei Han ; PMLR 31:307-315

DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes

Abner Guzman-Rivera,  Pushmeet Kohli,  Dhruv Batra ; PMLR 31:316-324

Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers

Jeffrey Ho,  Guang Cheng,  Hesamoddin Salehian,  Baba Vemuri ; PMLR 31:325-332

DYNACARE: Dynamic Cardiac Arrest Risk Estimation

Joyce Ho,  Yubin Park,  Carlos Carvalho,  Joydeep Ghosh ; PMLR 31:333-341

Active Learning for Interactive Visualization

Tomoharu Iwata,  Neil Houlsby,  Zoubin Ghahramani ; PMLR 31:342-350

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions

Prabhanjan Kambadur,  Aurelie Lozano ; PMLR 31:351-359

Beyond Sentiment: The Manifold of Human Emotions

Seungyeon Kim,  Fuxin Li,  Guy Lebanon,  Irfan Essa ; PMLR 31:360-369

Exact Learning of Bounded Tree-width Bayesian Networks

Janne Korhonen,  Pekka Parviainen ; PMLR 31:370-378

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables

Nevena Lazic,  Christopher Bishop,  John Winn ; PMLR 31:379-387

Structure Learning of Mixed Graphical Models

Jason Lee,  Trevor Hastie ; PMLR 31:388-396

Dynamic Scaled Sampling for Deterministic Constraints

Lei Li,  Bharath Ramsundar,  Stuart Russell ; PMLR 31:397-405

Learning Markov Networks With Arithmetic Circuits

Daniel Lowd,  Amirmohammad Rooshenas ; PMLR 31:406-414

Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions

Heng Luo,  Pierre Luc Carrier,  Aaron Courville,  Yoshua Bengio ; PMLR 31:415-423

Fast Near-GRID Gaussian Process Regression

Yuancheng Luo,  Ramani Duraiswami ; PMLR 31:424-432

Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling

Jianzhu Ma,  Jian Peng,  Sheng Wang,  Jinbo Xu ; PMLR 31:433-441

Thompson Sampling in Switching Environments with Bayesian Online Change Detection

Joseph Mellor,  Jonathan Shapiro ; PMLR 31:442-450

A Last-Step Regression Algorithm for Non-Stationary Online Learning

Edward Moroshko,  Koby Crammer ; PMLR 31:451-462

Competing with an Infinite Set of Models in Reinforcement Learning

Phuong Nguyen,  Odalric-Ambrym Maillard,  Daniil Ryabko,  Ronald Ortner ; PMLR 31:463-471

Efficient Variational Inference for Gaussian Process Regression Networks

Trung Nguyen,  Edwin Bonilla ; PMLR 31:472-480

High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers

Zheng Pan,  Changshui Zhang ; PMLR 31:481-488

Bayesian Structure Learning for Functional Neuroimaging

Mijung Park,  Oluwasanmi Koyejo,  Joydeep Ghosh,  Russell Poldrack,  Jonathan Pillow ; PMLR 31:489-497

Random Projections for Support Vector Machines

Saurabh Paul,  Christos Boutsidis,  Malik Magdon-Ismail,  Petros Drineas ; PMLR 31:498-506

Distribution-Free Distribution Regression

Barnabas Poczos,  Aarti Singh,  Alessandro Rinaldo,  Larry Wasserman ; PMLR 31:507-515

Localization and Adaptation in Online Learning

Alexander Rakhlin,  Ohad Shamir,  Karthik Sridharan ; PMLR 31:516-526

A recursive estimate for the predictive likelihood in a topic model

James Scott,  Jason Baldridge ; PMLR 31:527-535

Detecting Activations over Graphs using Spanning Tree Wavelet Bases

James Sharpnack,  Aarti Singh,  Akshay Krishnamurthy ; PMLR 31:536-544

Changepoint Detection over Graphs with the Spectral Scan Statistic

James Sharpnack,  Aarti Singh,  Alessandro Rinaldo ; PMLR 31:545-553

Central Limit Theorems for Conditional Markov Chains

Mathieu Sinn,  Bei Chen ; PMLR 31:554-562

Statistical Tests for Contagion in Observational Social Network Studies

Greg Ver Steeg,  Aram Galstyan ; PMLR 31:563-571

Completeness Results for Lifted Variable Elimination

Nima Taghipour,  Daan Fierens,  Guy Van den Broeck,  Jesse Davis,  Hendrik Blockeel ; PMLR 31:572-580

Supervised Sequential Classification Under Budget Constraints

Kirill Trapeznikov,  Venkatesh Saligrama ; PMLR 31:581-589

On the Asymptotic Optimality of Maximum Margin Bayesian Networks

Sebastian Tschiatschek,  Franz Pernkopf ; PMLR 31:590-598

Collapsed Variational Bayesian Inference for Hidden Markov Models

Pengyu Wang,  Phil Blunsom ; PMLR 31:599-607

Block Regularized Lasso for Multivariate Multi-Response Linear Regression

Weiguang Wang,  Yingbin Liang,  Eric Xing ; PMLR 31:608-617

Bethe Bounds and Approximating the Global Optimum

Adrian Weller,  Tony Jebara ; PMLR 31:618-631

Dual Decomposition for Joint Discrete-Continuous Optimization

Christopher Zach ; PMLR 31:632-640

Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes

Ke Zhou,  Hongyuan Zha,  Le Song ; PMLR 31:641-649

Greedy Bilateral Sketch, Completion & Smoothing

Tianyi Zhou,  Dacheng Tao ; PMLR 31:650-658

Scoring anomalies: a M-estimation formulation

Stéphan Clémençon,  Jérémie Jakubowicz ; PMLR 31:659-667

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