Accepted submissions
- A Decoupled Approach to Exemplar-based Unsupervised Learning.
Sebastian Nowozin and Gökhan Bakir - A Distance Model for Rhythms.
Jean-Francois Paiement, Yves Grandvalet, Samy Bengio and Douglas Eck - A Dual Coordinate Descent Method for Large-scale Linear SVM.
Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi and S. Sundararajan - A Generalization of Haussler's Convolution Kernel - Mapping Kernel.
Kilho Shin and Tetsuji Kuboyama - A Least Squares Formulation for Canonical Correlation Analysis.
Liang Sun, Shuiwang Ji and Jieping Ye - A Quasi-Newton Approach to Nonsmooth Convex Optimization.
Jin Yu, S.V.N. Vishwanathan, Simon Guenter and Nicol Schraudolph - A Rate-Distortion One-Class Model and its Applications to Clustering.
Koby Crammer, Partha Pratim Talukdar and Fernando Pereira - A Reproducing Kernel Hilbert Space Framework for Pairwise Time Series Distances.
Zhengdong Lu, Todd K. Leen, Yonghong Huang and Deniz Erdogmus - A Semi-parametric Statistical Approach to Model-free Policy Evaluation.
Tsuyoshi Ueno, Motoaki Kawanabe, Takeshi Mori, Shin-Ichi Maeda and Shin Ishii - A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning.
Ronan Collobert and Jason Weston - A Worst-Case Comparison Between Temporal Difference and Residual Gradient with Linear Function Approximation.
Lihong Li - Accurate Max-margin Training for Structured Output Spaces.
Sunita Sarawagi and Rahul Gupta - Active Kernel Learning.
Steven C.H. Hoi and Rong Jin - Active Reinforcement Learning.
Arkady Epshteyn, Adam Vogel and Gerald DeJong - Actively Learning Level-Sets of Composite Functions.
Brent Bryan and Jeff Schneider - Adaptive p-Posterior Mixture-Model Kernels for Multiple Instance Learning.
Hua-Yan Wang, Qiang Yang and Hongbin Zha - An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning.
Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield and Michael Littman - An Analysis of Reinforcement Learning with Function Approximation.
Francisco Melo, Sean Meyn and Isabel Ribeiro - An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators.
Percy Liang and Michael Jordan - An Empirical Evaluation of Supervised Learning in High Dimensions.
Rich Caruana, Nikos Karampatziakis and Ainur Yessenalina - An HDP-HMM for Systems with State Persistence.
Emily Fox, Erik Sudderth, Michael Jordan and Alan Willsky - An Object-Oriented Representation for Efficient Reinforcement Learning.
Carlos Diuk, Andre Cohen and Michael Littman - An RKHS for Multi-View Learning and Manifold Co-Regularization.
Vikas Sindhwani and David Rosenberg - Apprenticeship Learning Using Linear Programming.
Umar Syed, Michael Bowling and Robert Schapire - Automatic Discovery and Transfer of MAXQ Hierarchies.
Neville Mehta, Soumya Ray, Prasad Tadepalli and Thomas Dietterich - Autonomous Geometric Precision Error Estimation in Low-level Computer Vision Tasks.
Andrés Corrada-Emmanuel and Howard Schultz - Bayes Optimal Classification for Decision Trees.
Siegfried Nijssen - Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer.
Vikas Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar and R. Bharat Rao - Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo.
Ruslan Salakhutdinov and Andriy Mnih - Beam Sampling for the Infinite Hidden Markov Model.
Jurgen Van Gael, Yunus Saatci, Yee Whye Teh and Zoubin Ghahramani - Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression.
Saharon Rosset - Bolasso: Model Consistent Lasso Estimation through the Bootstrap.
Francis Bach - Boosting with Incomplete Information.
Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori and Feng Jiao - Causal Modelling Combining Instantaneous and Lagged Effects: an Identifiable Model Based on Non-Gaussianity.
Aapo Hyvarinen, Shohei Shimizu and Patrik Hoyer - Classification using Discriminative Restricted Boltzmann Machines.
Hugo Larochelle and Yoshua Bengio - Closed-form Supervised Dimensionality Reduction with Generalized Linear Models.
Irina Rish, Genady Grabarnilk, Guillermo Cecchi, Francisco Pereira and Geoffrey J. Gordon - Composite Kernel Learning.
Marie Szafranski, Yves Grandvalet and Alain Rakotomamonjy - Compressed Sensing and Bayesian Experimental Design.
Matthias Seeger and Hannes Nickisch - Confidence-Weighted Linear Classification.
Mark Dredze, Koby Crammer and Fernando Pereira - Cost-Sensitive Multi-class Classification from Probability Estimates.
Deirdre O'Brien, Maya Gupta and Robert Gray - Data Spectroscopy: Learning Mixture Models using Eigenspaces of Convolution Operators.
Tao Shi, Mikhail Belkin and Bin Yu - Deep Learning via Semi-Supervised Embedding.
Jason Weston, Frédéric Ratle and Ronan Collobert - Democratic Approximation of Lexicographic Preference Models.
Fusun Yaman, Thomas Walsh, Michael Littman and Marie desJardins - Detecting Statistical Interactions with Additive Groves of Trees.
Daria Sorokina, Rich Caruana, Mirek Riedewald and Daniel Fink - Dirichlet Component Analysis: Feature Extraction for Compositional Data.
Hua-Yan Wang, Qiang Yang, Hong Qin and Hongbin Zha - Discriminative Parameter Learning for Bayesian Networks.
Jiang Su, Harry Zhang, Charles X. Ling and Stan Matwin - Discriminative Structure and Parameter Learning for Markov Logic Networks.
Tuyen Huynh and Raymond Mooney - Efficient Bandit Algorithms for Online Multiclass Prediction.
Sham M. Kakade, Shai Shalev-Shwartz and Ambuj Tewari - Efficient MultiClass Maximum Margin Clustering.
Bin Zhao, Fei Wang and Changshui Zhang - Efficient Projections onto the L1-Ball for Learning in High Dimensions.
John Duchi, Shai Shalev-Shwartz, Yoram Singer and Tushar Chandra - Efficiently Learning Linear-Linear Exponential Family Predictive Representations of State.
David Wingate and Satinder Singh - Efficiently Solving Convex Relaxations for MAP Estimation.
Pawan Kumar Mudigonda and Philip Torr - Empirical Bernstein Stopping.
Volodymyr Mnih, Csaba Szepesvari and Jean-Yves Audibert - Estimating Labels from Label Proportions.
Novi Quadrianto, Alex Smola, Tiberio Caetano and Quoc Viet Le - Estimating Local Optimums in EM Algorithm over Gaussian Mixture Model.
Zhenjie Zhang, Bing Tian Dai and Anthony K.H. Tung - Expectation-Maximization for Sparse and Non-Negative PCA.
Christian David Sigg and Joachim M. Buhmann - Exploration Scavenging.
John Langford, Alexander Strehl and Jennifer Wortman - Extracting and Composing Robust Features with Denoising Autoencoders.
Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol - Fast Estimation of Relational Pattern Coverage through Randomization and Maximum Likelihood.
Ondrej Kuzelka and Filip Zelezny - Fast Gaussian Process Methods for Point Process Intensity Estimation.
John Cunningham, Krishna Shenoy and Maneesh Sahani - Fast Incremental Proximity Search in Large Graphs.
Purnamrita Sarkar, Andrew Moore and Amit Prakash - Fast Nearest Neighbor Retrieval for Bregman Divergences.
Lawrence Cayton - Fast Solvers and Efficient Implementations for Distance Metric Learning.
Kilian Weinberger and Lawrence Saul - Fast Support Vector Machine Training and Classification on Graphics Processors.
Bryan Catanzaro, Narayanan Sundaram and Kurt Keutzer - Fully Distributed EM for Very Large Datasets.
Jason Wolfe, Aria Haghighi and Dan Klein - Gaussian Process Product Models for Nonparametric Nonstationarity.
Ryan Adams and Oliver Stegle - Graph Kernels Between Point Clouds.
Francis Bach - Graph Transduction via Alternating Minimization.
Jun Wang, Tony Jebara and Shih-Fu Chang - Grassmann Discriminant Analysis: a Unifying View on Subspace-Based Learning.
Jihun Hamm and Daniel Lee - Hierarchical Kernel Stick-Breaking Process for Multi-Task Image Analysis.
Qi An, Chunping Wang, Ivo Shterev, Eric Wang, Lawrence Carin and David B. Dunson - Hierarchical Model-Based Reinforcement Learning: R-max + MAXQ.
Nicholas Jong and Peter Stone - Hierarchical sampling for active learning.
Sanjoy Dasgupta and Daniel Hsu - ICA and ISA Using Schweizer-Wolff Measure of Dependence.
Sergey Kirshner and Barnabás Póczos - Improved Nystrom Low-Rank Approximation and Error Analysis.
Kai Zhang, Ivor Tsang and James Kwok - Inverting the Viterbi Algorithm: an Abstract Framework for Structure Design.
Michael Schnall-Levin, Leonid Chindelevitch and Bonnie Berger - Knows What It Knows: A Framework For Self-Aware Learning.
Lihong Li, Michael Littman and Thomas Walsh - Laplace Maximum Margin Markov Networks.
Jun Zhu, Eric Xing and Bo Zhang - Large Scale Manifold Transduction.
Michael Karlen, Jason Weston, Ayse Erkan and Ronan Collobert - Learning All Optimal Policies with Multiple Criteria.
Leon Barrett and Srinivas Narayanan - Learning Dissimilarities by Ranking: From SDP to QP.
Hua Ouyang and Alexander Gray - Learning Diverse Rankings with Multi-Armed Bandits.
Filip Radlinski, Robert Kleinberg and Thorsten Joachims - Learning for Control from Multiple Demonstrations.
Adam Coates, Pieter Abbeel and Andrew Ng - Learning from Incomplete Data with Infinite Imputations.
Uwe Dick, Peter Haider and Tobias Scheffer - Learning to Classify with Missing and Corrupted Features.
Ofer Dekel and Ohad Shamir - Learning to Learn Implicit Queries from Gaze Patterns.
Kai Puolamäki, Antti Ajanki and Samuel Kaski - Learning to Sportscast: A Test of Grounded Language Acquisition.
David Chen and Raymond Mooney - Listwise Approach to Learning to Rank - Theory and Algorithm.
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang and Hang Li - Local Likelihood Modeling of Temporal Text Streams.
Guy Lebanon and Yang Zhao - Localized Multiple Kernel Learning.
Mehmet Gonen and Ethem Alpaydin - Manifold Alignment using Procrustes Analysis.
Chang Wang and Sridhar Mahadevan - ManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning.
Nicolas Loeff, David Forsyth and Deepak Ramachandran - Maximum Likelihood Rule Ensembles.
Wojciech Kotlowski, Krzysztof Dembczynski and Roman Slowinski - Memory Bounded Inference in Topic Models.
Ryan Gomes, Max Welling and Pietro Perona - Message-passing for Graph-structured Linear Programs: Proximal Projections, Convergence and Rounding Schemes.
Pradeep Ravikumar, Alekh Agarwal and Martin J. Wainwright - Metric Embedding for Kernel Classification Rules.
Bharath Sriperumbudur, Omer Lang and Gert Lanckriet - Modeling Interleaved Hidden Processes.
Niels Landwehr - Modified MMI/MPE: a Direct Evaluation of the Margin in Speech Recognition.
Georg Heigold, Thomas Deselaers, Ralf Schlueter and Hermann Ney - mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations.
Suyash Shringarpure and Eric Xing - Multi-Classification by Categorical Features via Clustering.
Yevgeny Seldin and Naftali Tishby - Multi-Task Compressive Sensing with Dirichlet Process Priors.
Yuting Qi, Dehong Liu, David Dunson and Lawrence Carin - Multi-Task Learning for HIV Therapy Screening.
Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer and Tobias Scheffer - Multiple Instance Ranking.
Charles Bergeron, Jed Zaretzki, Curt Breneman and Kristin Bennett - Nearest Hyperdisk Methods for High-Dimensional Classification.
Hakan Cevikalp, Bill Triggs and Robi Polikar - No-Regret Learning in Convex Games.
Geoffrey J. Gordon, Amy Greenwald and Casey Marks - Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains.
Kristian Kersting and Kurt Driessens - Nonextensive Entropic Kernels.
Andre F. T. Martins, Mario A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith and Eric P. Xing - Nonnegative Matrix Factorization via Rank-One Downdate.
Michael Biggs, Ali Ghodsi and Stephen Vavasis - Nu-Support Vector Machine as Conditional Value-at-Risk Minimization.
Akiko Takeda and Masashi Sugiyama - On Multi-View Active Learning and the Combination with Semi-Supervised Learning.
Wei Wang and Zhi-Hua Zhou - On Partial Optimality in Multi-label MRFs.
Pushmeet Kohli, Alexander Shekhovtsov, Carsten Rother, Vladimir Kolmogorov and Philip Torr - On the Chance Accuracies of Large Collections of Classifiers.
Mark Palatucci and Andrew Carlson - On the Hardness of Finding Symmetries in Markov Decision Processes.
Shravan Narayanamurthy and Balaraman Ravindran - On the Quantitative Analysis of Deep Belief Networks.
Ruslan Salakhutdinov and Iain Murray - On-line Discovery of Temporal-Difference Networks.
Takaki Makino and Toshihisa Takagi - Online Kernel Selection for Bayesian Reinforcement Learning.
Joseph Reisinger, Peter Stone and Risto Miikkulainen - Optimized Cutting Plane Algorithm for Support Vector Machines.
Vojtech Franc and Soeren Sonnenburg - Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning.
Pinar Donmez and Jaime Carbonell - Pairwise Constraint Propagation by Semidefinite Programming for Semi-Supervised Classification.
Zhenguo Li, Jianzhuang Liu and Xiaoou Tang - Pointwise Exact Bootstrap Distributions of Cost Curves.
Charles Dugas and David Gadoury - Polyhedral Classifier for Target Detection A Case Study: Colorectal Cancer.
Murat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff and Vikas C. Raykar - Preconditioned Temporal Difference Learning.
Hengshuai Yao and Zhi-Qiang Liu - Predicting Diverse Subsets Using Structural SVMs.
Yisong Yue and Thorsten Joachims - Prediction with Expert Advice for the Brier Game.
Vladimir Vovk and Fedor Zhdanov - Privacy-Preserving Reinforcement Learning.
Jun Sakuma, Shigenobu Kobayashi and Rebecca Wright - Query-Level Stability and Generalization in Learning to Rank.
Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma and Hang Li - Random Classification Noise Defeats All Convex Potential Boosters.
Philip M. Long and Rocco A. Servedio - Rank Minimization via Online Learning.
Raghu Meka, Prateek Jain, Constantine Caramanis and Inderjit Dhillon - Reinforcement Learning in the Presence of Rare Events.
Jordan Frank, Shie Mannor and Doina Precup - Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.
Finale Doshi, Joelle Pineau and Nicholas Roy - Robust Matching and Recognition using Context-Dependent Kernels.
Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa and Renaud Keriven - SVM Optimization: Inverse Dependence on Training Set Size.
Shai Shalev-Shwartz and Nathan Srebro - Sample-Based Learning and Search with Permanent and Transient Memories.
David Silver, Richard Sutton and Martin Mueller - Self-taught Clustering.
Wenyuan Dai, Qiang Yang, Gui-Rong Xue and Yong Yu - Semi-supervised Learning of Compact Document Representations with Deep Networks.
Marc'Aurelio Ranzato and Martin Szummer - Sequence Kernels for Predicting Protein Essentiality.
Cyril Allauzen, Mehryar Mohri and Ameet Talwalkar - Space-indexed Dynamic Programming: Learning to Follow Trajectories.
J. Zico Kolter, Adam Coates, Andrew Ng, Yi Gu and Charles DuHadway - Sparse Bayesian Nonparametric Regression.
Francois Caron and Arnaud Doucet - Sparse Multiscale Gaussian Process Regression.
Christian Walder, Kwang In Kim and Bernhard Schoelkopf - Spectral Clustering with Inconsistent Advice.
Tom Coleman, James Saunderson and Anthony Wirth - Stability of Transductive Regression Algorithms.
Corinna Cortes, Mehryar Mohri, Dmitry Pechyony and Ashish Rastogi - Statistical Models for Partial Membership.
Katherine Heller, Sinead Williamson and Zoubin Ghahramani - Stopping Conditions for Exact Computation of Leave-One-Out Error in Support Vector Machines.
Vojtech Franc, Pavel Laskov and Klaus-R. Mueller - Strategy Evaluation in Extensive Games with Importance Sampling.
Michael Bowling, Michael Johanson, Neil Burch and Duane Szafron - Structure Compilation: Trading Structure for Features.
Percy Liang, Hal Daume and Dan Klein - Tailoring Density Estimation via Reproducing Kernel Moment Matching.
Le Song, Xinhua Zhang, Alex Smola, Arthur Gretton and Bernhard Schoelkopf - The Asymptotics of Semi-Supervised Learning in Discriminative Probabilistic Models.
Nataliya Sokolovska, Olivier Cappé and François Yvon - The Dynamic Hierarchical Dirichlet Process.
Lu Ren, David B. Dunson and Lawrence Carin - The GroupLASSO for Generalized Linear Models: Uniqueness of Solutions and Efficient Algorithms.
Volker Roth and Bernd Fischer - The Many Faces of Optimism: a Unifying Approach.
Istvan Szita and Andras Lorincz - The Projectron: a Bounded Kernel-Based Perceptron.
Francesco Orabona, Joseph Keshet and Barbara Caputo - The Skew Spectrum of Graphs.
Risi Kondor and Karsten Borgwardt - Topologically-Constrained Latent Variable Models.
Raquel Urtasun, David Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell and Neil Lawrence - Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient.
Tijmen Tieleman - Training SVM with Indefinite Kernels.
Jianhui Chen and Jieping Ye - Training Structural SVMs when Exact Inference is Intractable.
Thomas Finley and Thorsten Joachims - Transfer of Samples in Batch Reinforcement Learning.
Alessandro Lazaric, Marcello Restelli and Andrea Bonarini - Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization.
Haiping Lu, Konstantinos Plataniotis and Anastasios Venetsanopoulos - Unsupervised Rank Aggregation with Distance-Based Models.
Alexandre Klementiev, Dan Roth and Kevin Small
158 papers