NIPS 2007: Vancouver, British Columbia, Canada

• Misha Ahrens, Maneesh Sahani:
  Inferring Elapsed Time from Stochastic Neural Processes.
  
• András Antos, Rémi Munos, Csaba Szepesvári:
  Fitted Q-iteration in continuous action-space MDPs.
  
• Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
  Variational Inference for Diffusion Processes.
  
• Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
  A Spectral Regularization Framework for Multi-Task Structure Learning.
  
• Christopher G. Atkeson, Benjamin Stephens:
  Random Sampling of States in Dynamic Programming.
  
• Jean-Yves Audibert:
  Progressive mixture rules are deviation suboptimal.
  
• Francis Bach, Zaïd Harchaoui:
  DIFFRAC: a discriminative and flexible framework for clustering.
  
• Marco Barreno, Alvaro A. Cárdenas, J. Doug Tygar:
  Optimal ROC Curve for a Combination of Classifiers.
  
• Peter L. Bartlett, Elad Hazan, Alexander Rakhlin:
  Adaptive Online Gradient Descent.
  
• Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio:
  One-Pass Boosting.
  
• Ulrik Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma:
  Comparing Bayesian models for multisensory cue combination without mandatory integration.
  
• Pietro Berkes, Richard Turner, Maneesh Sahani:
  On Sparsity and Overcompleteness in Image Models.
  
• Matthias Bethge, Philipp Berens:
  Near-Maximum Entropy Models for Binary Neural Representations of Natural Images.
  
• Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee:
  Incremental Natural Actor-Critic Algorithms.
  
• Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller:
  Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing.
  
• David M. Blei, Jon D. McAuliffe:
  Supervised Topic Models.
  
• John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman:
  Learning Bounds for Domain Adaptation.
  
• Ben Blum, Michael I. Jordan, David Kim, Rhiju Das, Philip Bradley, David Baker:
  Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
  
• Edwin V. Bonilla, Kian Ming Chai, Chris Williams:
  Multi-task Gaussian Process Prediction.
  
• Léon Bottou, Olivier Bousquet:
  The Tradeoffs of Large Scale Learning.
  
• Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein:
  A Probabilistic Approach to Language Change.
  
• Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila:
  Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data.
  
• Joseph K. Bradley, Robert E. Schapire:
  FilterBoost: Regression and Classification on Large Datasets.
  
• Lars Buesing, Wolfgang Maass:
  Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons.
  
• Gertjan J. Burghouts, Arnold W. M. Smeulders, Jan-Mark Geusebroek:
  The Distribution Family of Similarity Distances.
  
• William Campbell, Fred Richardson:
  Discriminative Keyword Selection Using Support Vector Machines.
  
• Ben Carterette, Rosie Jones:
  Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks.
  
• Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa:
  Subspace-Based Face Recognition in Analog VLSI.
  
• Lawrence Cayton, Sanjoy Dasgupta:
  A learning framework for nearest neighbor search.
  
• Moran Cerf, Jonathan Harel, Wolfgang Einhäuser, Christof Koch:
  Predicting human gaze using low-level saliency combined with face detection.
  
• Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky:
  Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.
  
• Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui:
  Parallelizing Support Vector Machines on Distributed Computers.
  
• Nicolas Chapados, Yoshua Bengio:
  Augmented Functional Time Series Representation and Forecasting with Gaussian Processes.
  
• Anton Chechetka, Carlos Guestrin:
  Efficient Principled Learning of Thin Junction Trees.
  
• Ke Chen, Shihai Wang:
  Regularized Boost for Semi-Supervised Learning.
  
• Yuanhao Chen, Long Zhu, Chenxi Lin, Alan L. Yuille, HongJiang Zhang:
  Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing.
  
• Hai Leong Chieu, Wee Sun Lee, Yee Whye Teh:
  Cooled and Relaxed Survey Propagation for MRFs.
  
• Andreas Christmann, Ingo Steinwart:
  How SVMs can estimate quantiles and the median.
  
• Christoforos Christoforou, Paul Sajda, Lucas C. Parra:
  Second Order Bilinear Discriminant Analysis for single trial EEG analysis.
  
• Claudia Clopath, André Longtin, Wulfram Gerstner:
  An online Hebbian learning rule that performs Independent Component Analysis.
  
• John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
  Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.
  
• Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
  TrueSkill Through Time: Revisiting the History of Chess.
  
• Varsha Dani, Thomas P. Hayes, Sham Kakade:
  The Price of Bandit Information for Online Optimization.
  
• Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni:
  A general agnostic active learning algorithm.
  
• Justin Dauwels, François B. Vialatte, Tomasz M. Rutkowski, Andrzej Cichocki:
  Measuring Neural Synchrony by Message Passing.
  
• Nathaniel Daw, Aaron Courville:
  The rat as particle filter.
  
• Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng:
  Efficient multiple hyperparameter learning for log-linear models.
  
• Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen Green:
  Automatic Generation of Social Tags for Music Recommendation.
  
• Dominik Endres, Mike W. Oram, Johannes Schindelin, Peter Földiák:
  Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
  
• Gwenn Englebienne, Tim Cootes, Magnus Rattray:
  A probabilistic model for generating realistic lip movements from speech.
  
• Eric Brochu, Nando de Freitas, Abhijeet Ghosh:
  Active Preference Learning with Discrete Choice Data.
  
• Tim van Erven, Peter Grunwald, Steven de Rooij:
  Catching Up Faster in Bayesian Model Selection and Model Averaging.
  
• Saher Esmeir, Shaul Markovitch:
  Anytime Induction of Cost-sensitive Trees.
  
• Vittorio Ferrari, Andrew Zisserman:
  Learning Visual Attributes.
  
• Pierre W. Ferrez, José del R. Millán:
  EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection.
  
• Brian Fischer:
  Optimal models of sound localization by barn owls.
  
• Michael Frank, Noah Goodman, Joshua B. Tenenbaum:
  A Bayesian Framework for Cross-Situational Word-Learning.
  
• Peter Frazier, Angela Yu:
  Sequential Hypothesis Testing under Stochastic Deadlines.
  
• Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma:
  Learning the structure of manifolds using random projections.
  
• Charlie Frogner, Avi Pfeffer:
  Discovering Weakly-Interacting Factors in a Complex Stochastic Process.
  
• Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
  Kernel Measures of Conditional Dependence.
  
• Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos:
  The discriminant center-surround hypothesis for bottom-up saliency.
  
• Pierre Garrigues, Bruno Olshausen:
  Learning Horizontal Connections in a Sparse Coding Model of Natural Images.
  
• Michael Gashler, Dan Ventura, Tony R. Martinez:
  Iterative Non-linear Dimensionality Reduction with Manifold Sculpting.
  
• Claudio Gentile, Fabio Vitale, Cristian Brotto:
  On higher-order perceptron algorithms.
  
• Sebastian Gerwinn, Jakob Macke, Matthias Seeger, Matthias Bethge:
  Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.
  
• Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W. Adriaans:
  Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression.
  
• Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo Del Giudice:
  A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses .
  
• Amir Globerson, Tommi Jaakkola:
  Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
  
• Judy Goldsmith, Martin Mundhenk:
  Competition Adds Complexity.
  
• João Graça, Kuzman Ganchev, Ben Taskar:
  Expectation Maximization and Posterior Constraints.
  
• Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber:
  Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks.
  
• Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola:
  A Kernel Statistical Test of Independence.
  
• Yuhong Guo, Dale Schuurmans:
  Discriminative Batch Mode Active Learning.
  
• Yuhong Guo, Dale Schuurmans:
  Convex Relaxations of Latent Variable Training.
  
• Zaïd Harchaoui, Francis Bach, Eric Moulines:
  Testing for Homogeneity with Kernel Fisher Discriminant Analysis.
  
• Zaïd Harchaoui, Céline Lévy-Leduc:
  Catching Change-points with Lasso.
  
• Elad Hazan, Satyen Kale:
  Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria.
  
• Jingrui He, Jaime G. Carbonell:
  Nearest-Neighbor-Based Active Learning for Rare Category Detection.
  
• Chinmay Hegde, Michael B. Wakin, Richard G. Baraniuk:
  Random Projections for Manifold Learning.
  
• José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes:
  Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
  
• Peter Hoff:
  Modeling homophily and stochastic equivalence in symmetric relational data.
  
• Matthew Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
  Bayesian Policy Learning with Trans-Dimensional MCMC.
  
• Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.:
  Ultrafast Monte Carlo for Statistical Summations.
  
• Andrew Howard, Tony Jebara:
  Learning Monotonic Transformations for Classification.
  
• David Hsu, Wee Sun Lee, Nan Rong:
  What makes some POMDP problems easy to approximate?
  
• Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas:
  Efficient Inference for Distributions on Permutations.
  
• Marcus Hutter, Shane Legg:
  Temporal Difference Updating without a Learning Rate.
  
• Tony Jebara, Yingbo Song, Kapil Thadani:
  Density Estimation under Independent Similarly Distributed Sampling Assumptions.
  
• Michael Johanson, Martin Zinkevich, Michael H. Bowling:
  Computing Robust Counter-Strategies.
  
• Kyomin Jung, Devavrat Shah:
  Local Algorithms for Approximate Inference in Minor-Excluded Graphs.
  
• Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai:
  Multi-Task Learning via Conic Programming.
  
• Michael Kearns, Jinsong Tan, Jennifer Wortman:
  Privacy-Preserving Belief Propagation and Sampling.
  
• Charles Kemp, Noah Goodman, Joshua B. Tenenbaum:
  Learning and using relational theories.
  
• Sergey Kirshner:
  Learning with Tree-Averaged Densities and Distributions.
  
• J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng:
  Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
  
• Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta:
  Selecting Observations against Adversarial Objectives.
  
• Alex Kulesza, Fernando Pereira:
  Structured Learning with Approximate Inference.
  
• Krishnan Kumar, Chiru Bhattacharyya, Ramesh Hariharan:
  A Randomized Algorithm for Large Scale Support Vector Learning.
  
• John D. Lafferty, Larry A. Wasserman:
  Statistical Analysis of Semi-Supervised Regression.
  
• Yiu Man Lam, Bertram Shi:
  Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination.
  
• John Langford, Tong Zhang:
  The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information.
  
• Danial Lashkari, Polina Golland:
  Convex Clustering with Exemplar-Based Models.
  
• Alessandro Lazaric, Marcello Restelli, Andrea Bonarini:
  Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods.
  
• Guy Lebanon, Yi Mao:
  Non-parametric Modeling of Partially Ranked Data.
  
• Andrea Lecchini-Visintini, John Lygeros, Jan M. Maciejowski:
  Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains.
  
• Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng:
  Sparse deep belief net model for visual area V2.
  
• Robert A. Legenstein, Dejan Pecevski, Wolfgang Maass:
  Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
  
• Máté Lengyel, Peter Dayan:
  Hippocampal Contributions to Control: The Third Way.
  
• Ping Li, Trevor Hastie:
  A Unified Near-Optimal Estimator For Dimension Reduction in l_alpha(0 < alpha <= 2) Using Stable Random Projections.
  
• Ping Li, Christopher J. C. Burges, Qiang Wu:
  McRank: Learning to Rank Using Multiple Classification and Gradient Boosting.
  
• Percy Liang, Dan Klein, Michael I. Jordan:
  Agreement-Based Learning.
  
• Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel Lee:
  Blind channel identification for speech dereverberation using l1-norm sparse learning.
  
• Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
  Mining Internet-Scale Software Repositories.
  
• Qiuhua Liu, Xuejun Liao, Lawrence Carin:
  Semi-Supervised Multitask Learning.
  
• Philip M. Long, Rocco A. Servedio:
  Boosting the Area under the ROC Curve.
  
• Zhengdong Lu, Miguel Á. Carreira-Perpiñán, Cristian Sminchisescu:
  People Tracking with the Laplacian Eigenmaps Latent Variable Model.
  
• Ronny Luss, Alexandre d'Aspremont:
  Support Vector Machine Classification with Indefinite Kernels.
  
• Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann:
  Consistent Minimization of Clustering Objective Functions.
  
• Jakob Macke, Guenther Zeck, Matthias Bethge:
  Receptive Fields without Spike-Triggering.
  
• Maryam Mahdaviani, Tanzeem Choudhury:
  Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition.
  
• M. M. Mahmud, Sylvian R. Ray:
  Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations.
  
• Victoria Manfredi, Jim Kurose:
  Scan Strategies for Meteorological Radars.
  
• Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar:
  A neural network implementing optimal state estimation based on dynamic spike train decoding.
  
• Francois Meyer, Greg Stephens:
  Locality and low-dimensions in the prediction of natural experience from fMRI.
  
• Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi:
  Learning to classify complex patterns using a VLSI network of spiking neurons.
  
• Daichi Mochihashi, Eiichiro Sumita:
  The Infinite Markov Model.
  
• Mehryar Mohri, Afshin Rostamizadeh:
  Stability Bounds for Non-i.i.d. Processes.
  
• Michael Mozer, David Baldwin:
  Experience-Guided Search: A Theory of Attentional Control.
  
• Pawan Mudigonda, Vladimir Kolmogorov, Philip H. S. Torr:
  An Analysis of Convex Relaxations for MAP Estimation.
  
• Lawrence Murray, Amos J. Storkey:
  Continuous Time Particle Filtering for fMRI.
  
• Andrew Naish-Guzman, Sean B. Holden:
  Robust Regression with Twinned Gaussian Processes.
  
• Andrew Naish-Guzman, Sean B. Holden:
  The Generalized FITC Approximation.
  
• Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-Jacques E. Slotine, Rodney J. Douglas:
  Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons.
  
• David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling:
  Distributed Inference for Latent Dirichlet Allocation.
  
• XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
  Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
  
• Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii:
  Heterogeneous Component Analysis.
  
• Manfred Opper, Guido Sanguinetti:
  Variational inference for Markov jump processes.
  
• Luis E. Ortiz:
  CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation.
  
• Simon Osindero, Geoffrey E. Hinton:
  Modeling image patches with a directed hierarchy of Markov random fields.
  
• Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharyya, K. R. Ramakrishnan:
  Kernels on Attributed Pointsets with Applications.
  
• Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
  A Risk Minimization Principle for a Class of Parzen Estimators.
  
• Robert J. Peters, Laurent Itti:
  Congruence between model and human attention reveals unique signatures of critical visual events.
  
• Slav Petrov, Dan Klein:
  Discriminative Log-Linear Grammars with Latent Variables.
  
• Jonathan Pillow, Peter E. Latham:
  Neural characterization in partially observed populations of spiking neurons.
  
• John C. Platt, Emre Kiciman, David A. Maltz:
  Fast Variational Inference for Large-scale Internet Diagnosis.
  
• Ali Rahimi, Benjamin Recht:
  Random Features for Large-Scale Kernel Machines.
  
• Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun:
  Sparse Feature Learning for Deep Belief Networks.
  
• Vinayak Rao, Marc Howard:
  Retrieved context and the discovery of semantic structure.
  
• Pradeep D. Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
  SpAM: Sparse Additive Models.
  
• Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin:
  On Ranking in Survival Analysis: Bounds on the Concordance Index.
  
• Michael Ross, Andrew Cohen:
  GRIFT: A graphical model for inferring visual classification features from human data.
  
• Stéphane Ross, Brahim Chaib-draa, Joelle Pineau:
  Bayes-Adaptive POMDPs.
  
• Stéphane Ross, Joelle Pineau, Brahim Chaib-draa:
  Theoretical Analysis of Heuristic Search Methods for Online POMDPs.
  
• Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl:
  Learning the 2-D Topology of Images.
  
• Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio:
  Topmoumoute Online Natural Gradient Algorithm.
  
• Bryan C. Russell, Antonio B. Torralba, Ce Liu, Robert Fergus, William T. Freeman:
  Object Recognition by Scene Alignment.
  
• Ruslan Salakhutdinov, Andriy Mnih:
  Probabilistic Matrix Factorization.
  
• Ruslan Salakhutdinov, Geoffrey E. Hinton:
  Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
  
• Adam Sanborn, Thomas L. Griffiths:
  Markov Chain Monte Carlo with People.
  
• Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky:
  Linear programming analysis of loopy belief propagation for weighted matching.
  
• Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
  Message Passing for Max-weight Independent Set.
  
• Burr Settles, Mark Craven, Soumya Ray:
  Multiple-Instance Active Learning.
  
• Ohad Shamir, Naftali Tishby:
  Cluster Stability for Finite Samples.
  
• Tatyana Sharpee:
  Better than least squares: comparison of objective functions for estimating linear-nonlinear models.
  
• Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Smaragdis:
  Sparse Overcomplete Latent Variable Decomposition of Counts Data.
  
• Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen:
  Collective Inference on Markov Models for Modeling Bird Migration.
  
• Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon:
  A Constraint Generation Approach to Learning Stable Linear Dynamical Systems.
  
• Leonid Sigal, Alexandru O. Balan, Michael J. Black:
  Combined discriminative and generative articulated pose and non-rigid shape estimation.
  
• Ricardo Silva, Wei Chu, Zoubin Ghahramani:
  Hidden Common Cause Relations in Relational Learning.
  
• Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu:
  Ensemble Clustering using Semidefinite Programming.
  
• Kaushik Sinha, Mikhail Belkin:
  The Value of Labeled and Unlabeled Examples when the Model is Imperfect.
  
• Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf:
  An Analysis of Inference with the Universum.
  
• Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
  Bundle Methods for Machine Learning.
  
• Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton:
  Colored Maximum Variance Unfolding.
  
• David Sontag, Tommi Jaakkola:
  New Outer Bounds on the Marginal Polytope.
  
• Devarajan Sridharan, Brian Percival, John V. Arthur, Kwabena Boahen:
  An in-silico Neural Model of Dynamic Routing through Neuronal Coherence.
  
• Alan Stocker, Eero P. Simoncelli:
  A Bayesian Model of Conditioned Perception.
  
• Alexander L. Strehl, Michael L. Littman:
  Online Linear Regression and Its Application to Model-Based Reinforcement Learning.
  
• Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky:
  Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
  
• Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Von Bünau, Motoaki Kawanabe:
  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.
  
• Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu:
  Efficient Bayesian Inference for Dynamically Changing Graphs.
  
• Umar Syed, Robert E. Schapire:
  A Game-Theoretic Approach to Apprenticeship Learning.
  
• Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux:
  Hierarchical Penalization.
  
• Yuval Tassa, Tom Erez, William D. Smart:
  Receding Horizon Differential Dynamic Programming.
  
• Yee Whye Teh, Hal Daumé III, Daniel M. Roy:
  Bayesian Agglomerative Clustering with Coalescents.
  
• Yee Whye Teh, Kenichi Kurihara, Max Welling:
  Collapsed Variational Inference for HDP.
  
• Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola:
  Convex Learning with Invariances.
  
• Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart, David Levine, Freeman L. Rawson III, Charles Lefurgy:
  Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning.
  
• Ambuj Tewari, Peter L. Bartlett:
  Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs.
  
• Michalis K. Titsias:
  The Infinite Gamma-Poisson Feature Model.
  
• Kristina Toutanova, Mark Johnson:
  A Bayesian LDA-based model for semi-supervised part-of-speech tagging.
  
• Duan Tran, David Forsyth:
  Configuration Estimates Improve Pedestrian Finding.
  
• Eric K. C. Tsang, Bertram Emil Shi:
  Estimating disparity with confidence from energy neurons.
  
• Richard Turner, Maneesh Sahani:
  Modeling Natural Sounds with Modulation Cascade Processes.
  
• Jakob J. Verbeek, Bill Triggs:
  Scene Segmentation with CRFs Learned from Partially Labeled Images.
  
• Christian Walder, Olivier Chapelle:
  Learning with Transformation Invariant Kernels.
  
• Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
  Stable Dual Dynamic Programming.
  
• Xiaogang Wang, Eric Grimson:
  Spatial Latent Dirichlet Allocation.
  
• Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch:
  Boosting Algorithms for Maximizing the Soft Margin.
  
• Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola:
  COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
  
• Max Welling, Ian Porteous, Evgeniy Bart:
  Infinite State Bayes-Nets for Structured Domains.
  
• Ben Williams, Marc Toussaint, Amos J. Storkey:
  Modelling motion primitives and their timing in biologically executed movements.
  
• David Wingate, Satinder Singh Baveja:
  Exponential Family Predictive Representations of State.
  
• David P. Wipf, Srikantan Nagarajan:
  A New View of Automatic Relevance Determination.
  
• John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-Yeung Shum:
  Classification via Minimum Incremental Coding Length (MICL).
  
• Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu:
  Efficient Convex Relaxation for Transductive Support Vector Machine.
  
• Jieping Ye, Zheng Zhao, Mingrui Wu:
  Discriminative K-means for Clustering.
  
• Kai Yu, Wei Chu:
  Gaussian Process Models for Link Analysis and Transfer Learning.
  
• Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, Harald Steck, R. Bharat Rao:
  Bayesian Co-Training.
  
• Alan L. Yuille, Hongjing Lu:
  The Noisy-Logical Distribution and its Application to Causal Inference.
  
• Cha Zhang, Paul A. Viola:
  Multiple-Instance Pruning For Learning Efficient Cascade Detectors.
  
• Bing Zhao, Eric P. Xing:
  HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation.
  
• Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun:
  A General Boosting Method and its Application to Learning Ranking Functions for Web Search.
  
• Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
  Compressed Regression.
  
• Shenghuo Zhu, Kai Yu, Yihong Gong:
  Predictive Matrix-Variate t Models.
  
• Martin Zinkevich, Michael Johanson, Michael H. Bowling, Carmelo Piccione:
  Regret Minimization in Games with Incomplete Information.