NIPS 2004: Vancouver, British Columbia, Canada

• Michael H. Bowling:
  Convergence and No-Regret in Multiagent Learning.
  
• Pierre Baldi, Jianlin Cheng, Alessandro Vullo:
  Large-Scale Prediction of Disulphide Bond Connectivity.
  
• Jacob Goldberger, Sam T. Roweis:
  Hierarchical Clustering of a Mixture Model.
  
• Daniela Pucci de Farias, Nimrod Megiddo:
  Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments.
  
• Tong Zhang:
  Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification.
  
• Elizaveta Levina, Peter J. Bickel:
  Maximum Likelihood Estimation of Intrinsic Dimension.
  
• Alexander T. Ihler, John W. Fisher III, Alan S. Willsky:
  Message Errors in Belief Propagation.
  
• Alexei A. Efros, Volkan Isler, Jianbo Shi, Mirkó Visontai:
  Seeing through water.
  
• Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom M. Mitchell:
  Detecting Significant Multidimensional Spatial Clusters.
  
• Amnon Shashua, Tamir Hazan:
  Algebraic Set Kernels with Application to Inference Over Local Image Representations.
  
• Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov:
  Neighbourhood Components Analysis.
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Andrea Tironi, Luca Zaniboni:
  Incremental Algorithms for Hierarchical Classification.
  
• Guy Shani, Ronen I. Brafman:
  Resolving Perceptual Aliasing In The Presence Of Noisy Sensors.
  
• Mario Marchand, Mohak Shah:
  PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data.
  
• Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer:
  A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities.
  
• John M. Winn, Andrew Blake:
  Generative Affine Localisation and Tracking.
  
• Sajama, Alon Orlitsky:
  Semi-parametric Exponential Family PCA.
  
• Ingo Steinwart, Don R. Hush, Clint Scovel:
  Density Level Detection is Classification.
  
• Ingo Steinwart, Clint Scovel:
  Fast Rates to Bayes for Kernel Machines.
  
• Tobias Blaschke, Laurenz Wiskott:
  Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis.
  
• Ting Liu, Andrew W. Moore, Alexander G. Gray, Ke Yang:
  An Investigation of Practical Approximate Nearest Neighbor Algorithms.
  
• Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik:
  Parallel Support Vector Machines: The Cascade SVM.
  
• Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror:
  Result Analysis of the NIPS 2003 Feature Selection Challenge.
  
• Jinbo Bi, Tong Zhang:
  Support Vector Classification with Input Data Uncertainty.
  
• Dashan Gao, Nuno Vasconcelos:
  Discriminant Saliency for Visual Recognition from Cluttered Scenes.
  
• Corinna Cortes, Mehryar Mohri:
  Confidence Intervals for the Area Under the ROC Curve.
  
• Balaji Krishnapuram, David Williams, Ya Xue, Alexander J. Hartemink, Lawrence Carin, Mário A. T. Figueiredo:
  On Semi-Supervised Classification.
  
• Jieping Ye, Ravi Janardan, Qi Li:
  Two-Dimensional Linear Discriminant Analysis.
  
• Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky:
  Efficient Kernel Discriminant Analysis via QR Decomposition.
  
• Phillip Boyle, Marcus R. Frean:
  Dependent Gaussian Processes.
  
• Einat Klein, Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel:
  Synchronization of neural networks by mutual learning and its application to cryptography.
  
• Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik:
  Learning Hyper-Features for Visual Identification.
  
• Jian Zhang, Zoubin Ghahramani, Yiming Yang:
  A Probabilistic Model for Online Document Clustering with Application to Novelty Detection.
  
• Sander M. Bohte, Michael C. Mozer:
  Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity.
  
• Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira, Eisaku Maeda:
  Maximal Margin Labeling for Multi-Topic Text Categorization.
  
• Aharon Bar-Hillel, Adam Spiro, Eran Stark:
  Spike Sorting: Bayesian Clustering of Non-Stationary Data.
  
• Jongmin Kim, John J. Hopfield, Erik Winfree:
  Neural Network Computation by In Vitro Transcriptional Circuits.
  
• Johannes Mohr, Klaus Obermayer:
  A Topographic Support Vector Machine: Classification Using Local Label Configurations.
  
• Neil D. Lawrence, Michael I. Jordan:
  Semi-supervised Learning via Gaussian Processes.
  
• Alan L. Yuille:
  The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters.
  
• Alan L. Yuille:
  The Convergence of Contrastive Divergences.
  
• Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola:
  A Second Order Cone programming Formulation for Classifying Missing Data.
  
• Moray Allan, Christopher K. I. Williams:
  Harmonising Chorales by Probabilistic Inference.
  
• Yoshitatsu Matsuda, Kazunori Yamaguchi:
  Linear Multilayer Independent Component Analysis for Large Natural Scenes.
  
• Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
  The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees.
  
• Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
  Limits of Spectral Clustering.
  
• Felix Schürmann, Karlheinz Meier, Johannes Schemmel:
  Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid.
  
• Peter M. Todd, Anja Dieckmann:
  Heuristics for Ordering Cue Search in Decision Making.
  
• Jan Erik Solem, Fredrik Kahl:
  Surface Reconstruction using Learned Shape Models.
  
• Juan José del Coz, Gustavo F. Bayón, Jorge Díez, Oscar Luaces, Antonio Bahamonde, Carlos Sañudo:
  Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM.
  
• Volker Roth:
  Outlier Detection with One-class Kernel Fisher Discriminants.
  
• Philip M. Long, Xinyu Wu:
  Mistake Bounds for Maximum Entropy Discrimination.
  
• Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni:
  Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms.
  
• Michael Fink:
  Object Classification from a Single Example Utilizing Class Relevance Metrics.
  
• Wolfgang Maass, Robert A. Legenstein, Nils Bertschinger:
  Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits.
  
• Antti Honkela, Harri Valpola:
  Unsupervised Variational Bayesian Learning of Nonlinear Models.
  
• Fei Sha, Lawrence K. Saul:
  Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization.
  
• Liam Paninski:
  Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning.
  
• Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty:
  Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning.
  
• Oliver M. C. Williams, Andrew Blake, Roberto Cipolla:
  The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data.
  
• Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
  A Generalized Bradley-Terry Model: From Group Competition to Individual Skill.
  
• Constanze Hofstoetter, Manuel Gil, Kynan Eng, Giacomo Indiveri, Matti Mintz, Jörg Kramer, Paul F. M. J. Verschure:
  The Cerebellum Chip: an Analog VLSI Implementation of a Cerebellar Model of Classical Conditioning.
  
• Saharon Rosset:
  Following Curved Regularized Optimization Solution Paths.
  
• Robert Fergus, Andrew Zisserman, Pietro Perona:
  Sampling Methods for Unsupervised Learning.
  
• Matthias O. Franz, Bernhard Schölkopf:
  Implicit Wiener Series for Higher-Order Image Analysis.
  
• Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, Torbjørn Eltoft:
  The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space.
  
• Peter Sollich, Christopher K. I. Williams:
  Using the Equivalent Kernel to Understand Gaussian Process Regression.
  
• Fabio Aiolli, Alessandro Sperduti:
  Learning Preferences for Multiclass Problems.
  
• Taku Kudo, Eisaku Maeda, Yuji Matsumoto:
  An Application of Boosting to Graph Classification.
  
• Massimiliano Pavan, Marcello Pelillo:
  Efficient Out-of-Sample Extension of Dominant-Set Clusters.
  
• Bernd Fischer, Volker Roth, Joachim M. Buhmann, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz F. Roos, Peter Widmayer:
  A Hidden Markov Model for de Novo Peptide Sequencing.
  
• Nils Bertschinger, Thomas Natschläger, Robert A. Legenstein:
  At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks.
  
• Gökhan H. Bakir, Léon Bottou, Jason Weston:
  Breaking SVM Complexity with Cross-Training.
  
• Nathan Srebro, Noga Alon, Tommi Jaakkola:
  Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices.
  
• Maria-Florina Balcan, Avrim Blum, Ke Yang:
  Co-Training and Expansion: Towards Bridging Theory and Practice.
  
• Sophie Deneve:
  Bayesian inference in spiking neurons.
  
• Andrew Y. Ng, H. Jin Kim:
  Stable adaptive control with online learning.
  
• Rob Powers, Yoav Shoham:
  New Criteria and a New Algorithm for Learning in Multi-Agent Systems.
  
• Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Andrew Emili:
  Multiple Alignment of Continuous Time Series.
  
• Sunita Sarawagi, William W. Cohen:
  Semi-Markov Conditional Random Fields for Information Extraction.
  
• Shai Avidan, Moshe Butman:
  The power of feature clustering: An application to object detection.
  
• Wolf Kienzle, Gökhan H. Bakir, Matthias O. Franz, Bernhard Schölkopf:
  Face Detection - Efficient and Rank Deficient.
  
• Dan Pelleg, Andrew W. Moore:
  Active Learning for Anomaly and Rare-Category Detection.
  
• Changjiang Yang, Ramani Duraiswami, Larry S. Davis:
  Efficient Kernel Machines Using the Improved Fast Gauss Transform.
  
• Dori Peleg, Ron Meir:
  A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound.
  
• Thomas Navin Lal, Thilo Hinterberger, Guido Widman, Michael Schröder, N. Jeremy Hill, Wolfgang Rosenstiel, Christian Erich Elger, Bernhard Schölkopf, Niels Birbaumer:
  Methods Towards Invasive Human Brain Computer Interfaces.
  
• Bernhard Schölkopf, Joachim Giesen, Simon Spalinger:
  Kernel Methods for Implicit Surface Modeling.
  
• Liam Paninski:
  Variational Minimax Estimation of Discrete Distributions under KL Loss.
  
• Balázs Kégl, Ligen Wang:
  Boosting on Manifolds: Adaptive Regularization of Base Classifiers.
  
• Wojtek Kowalczyk, Nikos A. Vlassis:
  Newscast EM.
  
• Sean O'Rourke, Gal Chechik, Robin Friedman, Eleazar Eskin:
  Discrete profile alignment via constrained information bottleneck.
  
• Jean-Philippe Vert, Yoshihiro Yamanishi:
  Supervised Graph Inference.
  
• Peng Xu, Frederick Jelinek:
  Using Random Forests in the Structured Language Model.
  
• Shyam Visweswaran, Gregory F. Cooper:
  Instance-Specific Bayesian Model Averaging for Classification.
  
• Le Lu, Gregory D. Hager, Laurent Younes:
  A Three Tiered Approach for Articulated Object Action Modeling and Recognition.
  
• Giorgio Giacinto, Fabio Roli:
  Instance-Based Relevance Feedback for Image Retrieval.
  
• N. Jeremy Hill, Thomas Navin Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf:
  An Auditory Paradigm for Brain-Computer Interfaces.
  
• John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira:
  Hierarchical Distributed Representations for Statistical Language Modeling.
  
• Rasmus Kongsgaard Olsson, Lars Kai Hansen:
  A Harmonic Excitation State-Space Approach to Blind Separation of Speech.
  
• Finn Årup Nielsen:
  Mass Meta-analysis in Talairach Space.
  
• Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
  Parametric Embedding for Class Visualization.
  
• Sanjoy Dasgupta:
  Analysis of a greedy active learning strategy.
  
• Marcelo A. Montemurro, Stefano Panzeri:
  Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity.
  
• Yves Grandvalet, Yoshua Bengio:
  Semi-supervised Learning by Entropy Minimization.
  
• Ofer Shai, Brendan J. Frey, Quaid Morris, Qun Pan, Christine Misquitta, Benjamin J. Blencowe:
  Probabilistic Inference of Alternative Splicing Events in Microarray Data.
  
• Miguel Figueroa, Seth Bridges, Chris Diorio:
  On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks.
  
• Andrew McCallum, Ben Wellner:
  Conditional Models of Identity Uncertainty with Application to Noun Coreference.
  
• Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri:
  Economic Properties of Social Networks.
  
• Clayton Scott, Robert Nowak:
  On the Adaptive Properties of Decision Trees.
  
• Anthony J. Bell, Lucas C. Parra:
  Maximising Sensitivity in a Spiking Network.
  
• Frank DiMaio, Jude W. Shavlik, George N. Phillips:
  Pictorial Structures for Molecular Modeling: Interpreting Density Maps.
  
• Dengyong Zhou, Bernhard Schölkopf, Thomas Hofmann:
  Semi-supervised Learning on Directed Graphs.
  
• Pierre Moreels, Pietro Perona:
  Common-Frame Model for Object Recognition.
  
• Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
  Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection.
  
• Jaety Edwards, Yee Whye Teh, David A. Forsyth, Roger Bock, Michael Maire, Grace Vesom:
  Making Latin Manuscripts Searchable using gHMMs.
  
• Chakra Chennubhotla, Allan D. Jepson:
  Hierarchical Eigensolver for Transition Matrices in Spectral Methods.
  
• Jongwoo Lim, David A. Ross, Ruei-Sung Lin, Ming-Hsuan Yang:
  Incremental Learning for Visual Tracking.
  
• Lihi Zelnik-Manor, Pietro Perona:
  Self-Tuning Spectral Clustering.
  
• Ruei-Sung Lin, David A. Ross, Jongwoo Lim, Ming-Hsuan Yang:
  Adaptive Discriminative Generative Model and Its Applications.
  
• Dragomir Anguelov, Praveen Srinivasan, Hoi-Cheung Pang, Daphne Koller, Sebastian Thrun, James Davis:
  The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces.
  
• Joris M. Mooij, Hilbert J. Kappen:
  Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks.
  
• Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu:
  The Entire Regularization Path for the Support Vector Machine.
  
• Marco Cuturi, Jean-Philippe Vert:
  Semigroup Kernels on Finite Sets.
  
• Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola:
  Maximum-Margin Matrix Factorization.
  
• Jochen Triesch:
  Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons.
  
• Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, Okihide Hikosaka:
  Responding to Modalities with Different Latencies.
  
• Tanzeem Choudhury, Sumit Basu:
  Modeling Conversational Dynamics as a Mixed-Memory Markov Process.
  
• Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie:
  A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.
  
• Tamara L. Berg, Alexander C. Berg, Jaety Edwards, David A. Forsyth:
  Whos In the Picture.
  
• Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
  Assignment of Multiplicative Mixtures in Natural Images.
  
• Margarita Osadchy, Matthew L. Miller, Yann LeCun:
  Synergistic Face Detection and Pose Estimation with Energy-Based Models.
  
• Jing Wang, Zhenyue Zhang, Hongyuan Zha:
  Adaptive Manifold Learning.
  
• Aaron C. Courville, Nathaniel D. Daw, David S. Touretzky:
  Similarity and Discrimination in Classical Conditioning: A Latent Variable Account.
  
• Rajesh P. N. Rao:
  Hierarchical Bayesian Inference in Networks of Spiking Neurons.
  
• Angela J. Yu, Peter Dayan:
  Inference, Attention, and Decision in a Bayesian Neural Architecture.
  
• Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester:
  Exponentiated Gradient Algorithms for Large-margin Structured Classification.
  
• Khashayar Rohanimanesh, Robert Platt Jr., Sridhar Mahadevan, Roderic A. Grupen:
  Coarticulation in Markov Decision Processes.
  
• Francis R. Bach, Michael I. Jordan:
  Blind One-microphone Speech Separation: A Spectral Learning Approach.
  
• Amir Globerson, Gal Chechik, Fernando C. Pereira, Naftali Tishby:
  Euclidean Embedding of Co-Occurrence Data.
  
• Michael P. Holmes, Charles Lee Isbell Jr.:
  Schema Learning: Experience-Based Construction of Predictive Action Models.
  
• Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner:
  Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model.
  
• Lorenzo Rosasco, Andrea Caponnetto, Ernesto De Vito, Francesca Odone, Umberto De Giovannini:
  Learning, Regularization and Ill-Posed Inverse Problems.
  
• Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum:
  Integrating Topics and Syntax.
  
• Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet:
  A Direct Formulation for Sparse PCA Using Semidefinite Programming.
  
• R. Jacob Vogelstein, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, Ralph Etienne-Cummings:
  Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons.
  
• Hyun-Jin Park, Te-Won Lee:
  Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution.
  
• François Rivest, Yoshua Bengio, John Kalaska:
  Brain Inspired Reinforcement Learning.
  
• Mukund Narasimhan, Jeff A. Bilmes:
  Optimal sub-graphical models.
  
• Yuanqing Lin, Daniel D. Lee:
  Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation.
  
• Xiang Yan, Persi Diaconis, Paat Rusmevichientong, Benjamin Van Roy:
  Solitaire: Man Versus Machine.
  
• Pradeep Shenoy, Rajesh P. N. Rao:
  Dynamic Bayesian Networks for Brain-Computer Interfaces.
  
• Laurent Zwald, Régis Vert, Gilles Blanchard, Pascal Massart:
  Kernel Projection Machine: a New Tool for Pattern Recognition.
  
• Máté Lengyel, Peter Dayan:
  Rate- and Phase-coded Autoassociative Memory.
  
• Pascal Poupart, Craig Boutilier:
  VDCBPI: an Approximate Scalable Algorithm for Large POMDPs.
  
• Daniela Pucci de Farias, Benjamin Van Roy:
  A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees.
  
• David C. Parkes, Satinder P. Singh, Dimah Yanovsky:
  Approximately Efficient Online Mechanism Design.
  
• Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth:
  A Large Deviation Bound for the Area Under the ROC Curve.
  
• Yoshua Bengio, Martin Monperrus:
  Non-Local Manifold Tangent Learning.
  
• Shantanu Chakrabartty, Gert Cauwenberghs:
  Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation.
  
• Antonio B. Torralba, Kevin P. Murphy, William T. Freeman:
  Contextual Models for Object Detection Using Boosted Random Fields.
  
• Manfred Opper, Ole Winther:
  Expectation Consistent Free Energies for Approximate Inference.
  
• Baranidharan Raman, Ricardo Gutierrez-Osuna:
  Chemosensory Processing in a Spiking Model of the Olfactory Bulb: Chemotopic Convergence and Center Surround Inhibition.
  
• Scott Gaffney, Padhraic Smyth:
  Joint Probabilistic Curve Clustering and Alignment.
  
• Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan:
  Probabilistic Computation in Spiking Populations.
  
• Dustin Lang, Nando de Freitas:
  Beat Tracking the Graphical Model Way.
  
• Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse:
  Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging.
  
• Satinder P. Singh, Andrew G. Barto, Nuttapong Chentanez:
  Intrinsically Motivated Reinforcement Learning.
  
• Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton:
  Exponential Family Harmoniums with an Application to Information Retrieval.
  
• Joseph Bockhorst, Mark Craven:
  Markov Networks for Detecting Overalpping Elements in Sequence Data.
  
• David H. Stern, Thore Graepel, David J. C. MacKay:
  Modelling Uncertainty in the Game of Go.
  
• Eizaburo Doi, Michael S. Lewicki:
  Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units.
  
• Erik G. Learned-Miller, Parvez Ahammad:
  Joint MRI Bias Removal Using Entropy Minimization Across Images.
  
• Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei:
  Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
  
• Anton Schwaighofer, Volker Tresp, Kai Yu:
  Learning Gaussian Process Kernels via Hierarchical Bayes.
  
• Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp:
  Triangle Fixing Algorithms for the Metric Nearness Problem.
  
• Omid Madani, David M. Pennock, Gary William Flake:
  Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms.
  
• Michael D. Colagrosso, Michael C. Mozer:
  Theories of Access Consciousness.
  
• Francis R. Bach, Romain Thibaux, Michael I. Jordan:
  Computing regularization paths for learning multiple kernels.
  
• Eyal Even-Dar, Sham M. Kakade, Yishay Mansour:
  Experts in a Markov Decision Process.
  
• Miguel Á. Carreira-Perpiñán, Richard S. Zemel:
  Proximity Graphs for Clustering and Manifold Learning.
  
• Felix A. Wichmann, Arnulf B. A. Graf, Eero P. Simoncelli, Heinrich H. Bülthoff, Bernhard Schölkopf:
  Machine Learning Applied to Perception: Decision Images for Gender Classification.
  
• Kosuke Hamaguchi, Masato Okada, Kazuyuki Aihara:
  Theory of localized synfire chain: characteristic propagation speed of stable spike pattern.
  
• Erik Aurell, Uri Gordon, Scott Kirkpatrick:
  Comparing Beliefs, Surveys, and Random Walks.
  
• Charles A. Micchelli, Massimiliano Pontil:
  Kernels for Multi--task Learning.
  
• Ariadna Quattoni, Michael Collins, Trevor Darrell:
  Conditional Random Fields for Object Recognition.
  
• K. Y. Michael Wong, S. W. Lim, Zhuo Gao:
  Multi-agent Cooperation in Diverse Population Games.
  
• David P. Wipf, Bhaskar D. Rao:
  L_0-norm Minimization for Basis Selection.
  
• Sham M. Kakade, Andrew Y. Ng:
  Online Bounds for Bayesian Algorithms.
  
• Robert D. Kleinberg:
  Nearly Tight Bounds for the Continuum-Armed Bandit Problem.
  
• Evan C. Smith, Michael S. Lewicki:
  Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters.
  
• Linli Xu, James Neufeld, Bryce Larson, Dale Schuurmans:
  Maximum Margin Clustering.
  
• Roland Memisevic, Geoffrey E. Hinton:
  Multiple Relational Embedding.
  
• S. V. N. Vishwanathan, Alex J. Smola:
  Binet-Cauchy Kernels.
  
• Richard S. Sutton, Brian Tanner:
  Temporal-Difference Networks.
  
• Kumar Chellapilla, Patrice Y. Simard:
  Using Machine Learning to Break Visual Human Interaction Proofs (HIPs).
  
• Alan Stocker, Eero P. Simoncelli:
  Constraining a Bayesian Model of Human Visual Speed Perception.
  
• Olivier Chapelle, Zaïd Harchaoui:
  A Machine Learning Approach to Conjoint Analysis.
  
• Adrian Corduneanu, Tommi Jaakkola:
  Distributed Information Regularization on Graphs.
  
• Haidong Wang, Eran Segal, Asa Ben-Hur, Daphne Koller, Douglas L. Brutlag:
  Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale.
  
• Tim K. Marks, John Hershey, J. Cooper Roddey, Javier R. Movellan:
  Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters.
  
• Zhengdong Lu, Todd K. Leen:
  Semi-supervised Learning with Penalized Probabilistic Clustering.
  
• Laura Walker Renninger, James M. Coughlan, Preeti Verghese, Jitendra Malik:
  An Information Maximization Model of Eye Movements.
  
• Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thrun:
  Planning for Markov Decision Processes with Sparse Stochasticity.
  
• Balázs Kégl:
  Generalization Error and Algorithmic Convergence of Median Boosting.
  
• Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky:
  Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation.
  
• Rion Snow, Daniel Jurafsky, Andrew Y. Ng:
  Learning Syntactic Patterns for Automatic Hypernym Discovery.
  
• Pieter Abbeel, Andrew Y. Ng:
  Learning first-order Markov models for control.