Volume 29: Asian Conference on Machine Learning, 13-15 November 2013, Australian National University, Canberra, Australia


Editors: Cheng Soon Ong, Tu Bao Ho



Front Matter


Cheng Soon Ong, Tu Bao Ho ; PMLR 29:1-17

Accepted Papers

Stability of Multi-Task Kernel Regression Algorithms

Julien Audiffren, Hachem Kadri ; PMLR 29:1-16

Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions

Robert Durrant, Ata Kaban ; PMLR 29:17-32

Guided Monte Carlo Tree Search for Planning in Learned Environments

Jelle Van Eyck, Jan Ramon, Fabian Guiza, Geert MeyFroidt, Maurice Bruynooghe, Greet Van den Berghe ; PMLR 29:33-47

Linear Approximation to ADMM for MAP inference

Sholeh Forouzan, Alexander Ihler ; PMLR 29:48-61

Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification

Fabian Gieseke, Tapio Pahikkala, Christian Igel ; PMLR 29:62-71

Accelerated Coordinate Descent with Adaptive Coordinate Frequencies

Tobias Glasmachers, Urun Dogan ; PMLR 29:72-86

Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering

Xiaotian Jiang, Zhendong Niu, Jiamin Guo, Ghulam Mustafa, Zihan Lin, Baomi Chen, Qian Zhou ; PMLR 29:87-99

Multi-armed Bandit Problem with Lock-up Periods

Junpei Komiyama, Issei Sato, Hiroshi Nakagawa ; PMLR 29:100-115

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

Risheng Liu, Zhouchen Lin, Zhixun Su ; PMLR 29:116-132

Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine

Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh ; PMLR 29:133-148

Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes

Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda ; PMLR 29:149-164

Information Retrieval Perspective to Meta-visualization

Jaakko Peltonen, Ziyuan Lin ; PMLR 29:165-180

Achievability of Asymptotic Minimax Regret in Online and Batch Prediction

Kazuho Watanabe, Teemu Roos, Petri Myllymäki ; PMLR 29:181-196

Multi-Label Classification with Unlabeled Data: An Inductive Approach

Le Wu, Min-Ling Zhang ; PMLR 29:197-212

Q-learning for history-based reinforcement learning

Mayank Daswani, Peter Sunehag, Marcus Hutter ; PMLR 29:213-228

Multiclass Latent Locally Linear Support Vector Machines

Marco Fornoni, Barbara Caputo, Francesco Orabona ; PMLR 29:229-244

Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits

Nicolas Galichet, Michèle Sebag, Olivier Teytaud ; PMLR 29:245-260

The Multi-Task Learning View of Multimodal Data

Hachem Kadri, Stephane Ayache, Cécile Capponi, Sokol Koço, François-Xavier Dupé, Emilie Morvant ; PMLR 29:261-276

On multi-class classification through the minimization of the confusion matrix norm

Sokol Koço, Cécile Capponi ; PMLR 29:277-292

Generalized Aitchison Embeddings for Histograms

Tam Le, Marco Cuturi ; PMLR 29:293-308

Unconfused Ultraconservative Multiclass Algorithms

Ugo Louche, Liva Ralaivola ; PMLR 29:309-324

Second Order Online Collaborative Filtering

Jing Lu, Steven Hoi, Jialei Wang ; PMLR 29:325-340

Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning

Karim Abou- Moustafa, Dale Schuurmans, Frank Ferrie ; PMLR 29:341-356

Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels

Marion Neumann, Roman Garnett, Kristian Kersting ; PMLR 29:357-372

Aggregating Predictions via Sequential Mini-Trading

Mindika Premachandra, Mark Reid ; PMLR 29:373-387

Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking

Wei-Yuan Shen, Hsuan-Tien Lin ; PMLR 29:388-403

Multilabel Classification through Random Graph Ensembles

Hongyu Su, Juho Rousu ; PMLR 29:404-418

Improving Predictive Specificity of Description Logic Learners by Fortification

An Tran, Jens Dietrich, Hans Guesgen, Stephen Marsland ; PMLR 29:419-434

Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift

Thomas Vanck, Jochen Garcke ; PMLR 29:435-450

Locally-Linear Learning Machines (L3M)

Joseph Wang, Venkatesh Saligrama ; PMLR 29:451-466

Co-Training with Insufficient Views

Wei Wang, Zhi-Hua Zhou ; PMLR 29:467-482

EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning

Christian Wirth, Johannes Fürnkranz ; PMLR 29:483-497

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