NIPS 2005: Vancouver, British Columbia, Canada

• Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
  Learning vehicular dynamics, with application to modeling helicopters.
  
• Douglas Aberdeen:
  Policy-Gradient Methods for Planning.
  
• Felix V. Agakov, David Barber:
  Kernelized Infomax Clustering.
  
• Misha Ahrens, Quentin J. M. Huys, Liam Paninski:
  Large-scale biophysical parameter estimation in single neurons via constrained linear regression.
  
• Yasemin Altun, David A. McAllester, Mikhail Belkin:
  Margin Semi-Supervised Learning for Structured Variables.
  
• J. Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani:
  Large scale networks fingerprinting and visualization using the k-core decomposition.
  
• Brigham Anderson, Andrew W. Moore:
  Fast Information Value for Graphical Models.
  
• David Arathorn:
  A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects.
  
• Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
  Combining Graph Laplacians for Semi-Supervised Learning.
  
• John V. Arthur, Kwabena Boahen:
  Learning in Silicon: Timing is Everything.
  
• Michaël Aupetit:
  Learning Topology with the Generative Gaussian Graph and the EM Algorithm.
  
• J. Andrew Bagnell, Andrew Y. Ng:
  On Local Rewards and Scaling Distributed Reinforcement Learning.
  
• Chris Baker, Joshua B. Tenenbaum, Rebecca Saxe:
  Bayesian models of human action understanding.
  
• Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
  The Curse of Highly Variable Functions for Local Kernel Machines.
  
• Yoshua Bengio, Hugo Larochelle, Pascal Vincent:
  Non-Local Manifold Parzen Windows.
  
• Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte:
  Convex Neural Networks.
  
• Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir Spokoiny, Klaus-Robert Müller:
  Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction.
  
• Doron Blatt, Alfred O. Hero:
  From Weighted Classification to Policy Search.
  
• David M. Blei, John D. Lafferty:
  Correlated Topic Models.
  
• Neil D. B. Bruce, John K. Tsotsos:
  Saliency Based on Information Maximization.
  
• Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher Miller, Christopher Genovese, Larry A. Wasserman:
  Active Learning For Identifying Function Threshold Boundaries.
  
• Razvan C. Bunescu, Raymond J. Mooney:
  Subsequence Kernels for Relation Extraction.
  
• Rui Castro, Rebecca Willett, Robert Nowak:
  Faster Rates in Regression via Active Learning.
  
• Abdullah Celik, Milutin Stanacevic, Gert Cauwenberghs:
  Gradient Flow Independent Component Analysis in Micropower VLSI.
  
• Nicolò Cesa-Bianchi, Claudio Gentile:
  Improved risk tail bounds for on-line algorithms.
  
• Antoni B. Chan, Nuno Vasconcelos:
  Layered Dynamic Textures.
  
• Yixin Chen, Ya Zhang, Xiang Ji:
  Size Regularized Cut for Data Clustering.
  
• Koby Crammer, Michael J. Kearns, Jennifer Wortman:
  Learning from Data of Variable Quality.
  
• Márton Danóczy, Richard H. R. Hahnloser:
  Efficient estimation of hidden state dynamics from spike trains.
  
• Sanjoy Dasgupta:
  Coarse sample complexity bounds for active learning.
  
• Peter Dayan, Angela J. Yu:
  Norepinephrine and Neural Interrupts.
  
• Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
  The Forgetron: A Kernel-Based Perceptron on a Fixed Budget.
  
• Ofer Dekel, Yoram Singer:
  Data-Driven Online to Batch Conversions.
  
• Ricky Der, Daniel Lee:
  Beyond Gaussian Processes: On the Distributions of Infinite Networks.
  
• Inderjit S. Dhillon, Suvrit Sra:
  Generalized Nonnegative Matrix Approximations with Bregman Divergences.
  
• James Diebel, Sebastian Thrun:
  An Application of Markov Random Fields to Range Sensing.
  
• Chuong B. Do, Andrew Y. Ng:
  Transfer learning for text classification.
  
• Eizaburo Doi, Doru-Cristian Balcan, Michael S. Lewicki:
  A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels.
  
• Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus-Robert Müller:
  Optimizing spatio-temporal filters for improving Brain-Computer Interfacing.
  
• Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
  Correcting sample selection bias in maximum entropy density estimation.
  
• Jaety Edwards, David A. Forsyth:
  Searching for Character Models.
  
• Austin I. Eliazar, Ronald Parr:
  Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps.
  
• Yaakov Engel, Peter Szabó, Dmitry Volkinshtein:
  Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods.
  
• Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
  Two view learning: SVM-2K, Theory and Practice.
  
• Patrick Flaherty, Michael I. Jordan, Adam P. Arkin:
  Robust design of biological experiments.
  
• François Fleuret, Gilles Blanchard:
  Pattern Recognition from One Example by Chopping.
  
• Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang:
  Fast Krylov Methods for N-Body Learning.
  
• Brendan J. Frey, Delbert Dueck:
  Mixture Modeling by Affinity Propagation.
  
• Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
  Statistical Convergence of Kernel CCA.
  
• Glenn Fung, Rómer Rosales, Balaji Krishnapuram:
  Learning Rankings via Convex Hull Separation.
  
• Artur S. d'Avila Garcez, Luís C. Lamb, Dov M. Gabbay:
  A Connectionist Model for Constructive Modal Reasoning.
  
• Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan:
  Large-Scale Multiclass Transduction.
  
• Peter V. Gehler, Max Welling:
  Products of Edge-perts.
  
• Tao Geng, Bernd Porr, Florentin Wörgötter:
  Fast biped walking with a reflexive controller and real-time policy searching.
  
• Zoubin Ghahramani, Katherine A. Heller:
  Bayesian Sets.
  
• Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
  Query by Committee Made Real.
  
• Amir Globerson, Sam T. Roweis:
  Metric Learning by Collapsing Classes.
  
• Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
  Interpolating between types and tokens by estimating power-law generators.
  
• Yves Grandvalet, Johnny Mariéthoz, Samy Bengio:
  A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification.
  
• Thomas L. Griffiths, Zoubin Ghahramani:
  Infinite latent feature models and the Indian buffet process.
  
• Lacey Gunter, Ji Zhu:
  Computing the Solution Path for the Regularized Support Vector Regression.
  
• Firas Hamze, Nando de Freitas:
  Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs.
  
• Xiaofei He, Deng Cai, Partha Niyogi:
  Laplacian Score for Feature Selection.
  
• Xiaofei He, Deng Cai, Partha Niyogi:
  Tensor Subspace Analysis.
  
• Geoffrey E. Hinton, Vinod Nair:
  Inferring Motor Programs from Images of Handwritten Digits.
  
• Wentao Huang, Licheng Jiao, Tan Shan, Maoguo Gong:
  Response Analysis of Neuronal Population with Synaptic Depression.
  
• Yunsong Huang, B. Keith Jenkins:
  Non-iterative Estimation with Perturbed Gaussian Markov Processes.
  
• Jarmo Hurri:
  Learning Cue-Invariant Visual Responses.
  
• Laurent Itti, Pierre Baldi:
  Bayesian Surprise Attracts Human Attention.
  
• Herbert Jaeger, Mingjie Zhao, Andreas Kolling:
  Efficient Estimation of OOMs.
  
• Viren Jain, Valentin P. Zhigulin, H. Sebastian Seung:
  Representing Part-Whole Relationships in Recurrent Neural Networks.
  
• Rong Jin, Chris H. Q. Ding, Feng Kang:
  A Probabilistic Approach for Optimizing Spectral Clustering.
  
• Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky:
  Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation.
  
• Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman:
  Using epitomes to model genetic diversity: Rational design of HIV vaccines.
  
• Renaud Jolivet, Alexander Rauch, Hans-Rudolf Lüscher, Wulfram Gerstner:
  Integrate-and-Fire models with adaptation are good enough.
  
• Anatoli Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
  Generalization Error Bounds for Aggregation by Mirror Descent with Averaging.
  
• Sham M. Kakade, Matthias Seeger, Dean P. Foster:
  Worst-Case Bounds for Gaussian Process Models.
  
• Sham M. Kakade, Adam Kalai:
  From Batch to Transductive Online Learning.
  
• Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, Rosalind W. Picard:
  Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification.
  
• Yan Karklin, Michael S. Lewicki:
  Is Early Vision Optimized for Extracting Higher-order Dependencies?
  
• S. Sathiya Keerthi, Wei Chu:
  A matching pursuit approach to sparse Gaussian process regression.
  
• Mikaela Keller, Samy Bengio, Siew Yeung Wong:
  Benchmarking Non-Parametric Statistical Tests.
  
• Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd:
  Robust Fisher Discriminant Analysis.
  
• Kristina Lisa Klinkner, Cosma Rohilla Shalizi, Marcelo Camperi:
  Measuring Shared Information and Coordinated Activity in Neuronal Networks.
  
• O. Patrick Kreidl, Alan S. Willsky:
  Inference with Minimal Communication: a Decision-Theoretic Variational Approach.
  
• Eyal Krupka, Naftali Tishby:
  Generalization in Clustering with Unobserved Features.
  
• Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew Connolly:
  Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery.
  
• Malte Kuss, Carl Edward Rasmussen:
  Assessing Approximations for Gaussian Process Classification.
  
• John D. Lafferty, Larry A. Wasserman:
  Rodeo: Sparse Nonparametric Regression in High Dimensions.
  
• Kevin J. Lang:
  Fixing two weaknesses of the Spectral Method.
  
• Tilman Lange, Joachim M. Buhmann:
  Fusion of Similarity Data in Clustering.
  
• François Laviolette, Mario Marchand, Mohak Shah:
  A PAC-Bayes approach to the Set Covering Machine.
  
• Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp:
  Off-Road Obstacle Avoidance through End-to-End Learning.
  
• Dongryeol Lee, Alexander G. Gray, Andrew W. Moore:
  Dual-Tree Fast Gauss Transforms.
  
• Jung Hoon Lee, Xiaolong Ma, Konstantin Likharev:
  CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits.
  
• Robert A. Legenstein, Wolfgang Maass:
  A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity.
  
• Anna Levina, J. Michael Herrmann, Theo Geisel:
  Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches.
  
• Fan Li, Yiming Yang, Eric P. Xing:
  From Lasso regression to Feature vector machine.
  
• Xuejun Liao, Lawrence Carin:
  Radial Basis Function Network for Multi-task Learning.
  
• Lin Liao, Dieter Fox, Henry A. Kautz:
  Location-based activity recognition.
  
• Ross A. Lippert, Ryan M. Rifkin:
  Asymptotics of Gaussian Regularized Least Squares.
  
• Nicolas Loeff, Himanshu Arora, Alexander Sorokin, David A. Forsyth:
  Efficient Unsupervised Learning for Localization and Detection in Object Categories.
  
• Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
  Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.
  
• Hongjing Lu, Alan L. Yuille:
  Ideal Observers for Detecting Motion: Correspondence Noise.
  
• Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
  Principles of real-time computing with feedback applied to cortical microcircuit models.
  
• Sridhar Mahadevan, Mauro Maggioni:
  Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions.
  
• Uri Maoz, Elon Portugaly, Tamar Flash, Yair Weiss:
  Noise and the two-thirds power Law.
  
• Naoki Masuda, Shun-ichi Amari:
  Modeling Memory Transfer and Saving in Cerebellar Motor Learning.
  
• Samuel McClure, Mark S. Gilzenrat, Jonathan D. Cohen:
  An exploration-exploitation model based on norepinepherine and dopamine activity.
  
• Peter McCracken, Michael H. Bowling:
  Online Discovery and Learning of Predictive State Representations.
  
• Edward Meeds, Simon Osindero:
  An Alternative Infinite Mixture Of Gaussian Process Experts.
  
• Keiji Miura, Masato Okada, Shun-ichi Amari:
  Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments.
  
• Ciamac Cyrus Moallemi, Benjamin Van Roy:
  Consensus Propagation.
  
• Daichi Mochihashi, Yuji Matsumoto:
  Context as Filtering.
  
• Baback Moghaddam, Yair Weiss, Shai Avidan:
  Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms.
  
• Michael Mozer, Michael Shettel, Shaun Vecera:
  Top-Down Control of Visual Attention: A Rational Account.
  
• Tatsuto Murayama, Peter Davis:
  Rate Distortion Codes in Sensor Networks.
  
• Juan José Murillo-Fuentes, Sebatian Caro, Fernando Pérez-Cruz:
  Gaussian Processes for Multiuser Detection in CDMA receivers.
  
• Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling:
  Nested sampling for Potts models.
  
• Boaz Nadler, Stéphane Lafon, Ronald R. Coifman, Ioannis G. Kevrekidis:
  Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators.
  
• Srikantan S. Nagarajan, Hagai Attias, Kenneth E. Hild II, Kensuke Sekihara:
  Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity.
  
• Yusuke Nakashita, Yoshio Mita, Tadashi Shibata:
  An Analog Visual Pre-Processing Processor.
  
• Mukund Narasimhan, Nebojsa Jojic, Jeff Bilmes:
  Q-Clustering.
  
• Vidhya Navalpakkam, Laurent Itti:
  Optimal cue selection strategy.
  
• Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia:
  Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity.
  
• Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper:
  A Bayesian Spatial Scan Statistic.
  
• XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
  Divergences, surrogate loss functions and experimental design.
  
• Yael Niv, Nathaniel D. Daw, Peter Dayan:
  How fast to work: Response vigor, motivation and tonic dopamine.
  
• Guido Nolte, Andreas Ziehe, Frank C. Meinecke, Klaus-Robert Müller:
  Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction.
  
• Manfred Opper:
  An Approximate Inference Approach for the PCA Reconstruction Error.
  
• Gergo Orbán, József Fiser, Richard N. Aslin, Máté Lengyel:
  Bayesian model learning in human visual perception.
  
• Matthias Oster, Shih-Chii Liu:
  Spiking Inputs to a Winner-take-all Network.
  
• Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao:
  Variational EM Algorithms for Non-Gaussian Latent Variable Models.
  
• Liam Paninski:
  Nonparametric inference of prior probabilities from Bayes-optimal behavior.
  
• Ofer Pasternak, Nir A. Sochen, Nathan Intrator, Yaniv Assaf:
  Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI.
  
• Jean-Pascal Pfister, Wulfram Gerstner:
  Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects.
  
• Brian Potetz, Tai Sing Lee:
  Scaling Laws in Natural Scenes and the Inference of 3D Shape.
  
• Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder P. Singh:
  Off-policy Learning with Options and Recognizers.
  
• Maxim Raginsky, Svetlana Lazebnik:
  Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization.
  
• Pradeep D. Ravikumar, John D. Lafferty:
  Preconditioner Approximations for Probabilistic Graphical Models.
  
• Xiaofeng Ren, Charless Fowlkes, Jitendra Malik:
  Cue Integration for Figure/Ground Labeling.
  
• Teemu Roos, Peter Grünwald, Petri Myllymäki, Henry Tirri:
  Generalization to Unseen Cases.
  
• Benjamin Van Roy:
  TD(0) Leads to Better Policies than Approximate Value Iteration.
  
• Michele Rucci:
  Visual Encoding with Jittering Eyes.
  
• Purnamrita Sarkar, Andrew W. Moore:
  Dynamic Social Network Analysis using Latent Space Models.
  
• Eric Saund:
  Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours.
  
• Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng:
  Learning Depth from Single Monocular Images.
  
• Rory Sayres, David Ress, Kalanit Grill Spector:
  Identifying Distributed Object Representations in Human Extrastriate Visual Cortex.
  
• André van Schaik, Richard E. Reeve, Craig T. Jin, Tara Julia Hamilton:
  An aVLSI Cricket Ear Model.
  
• Michael Schmitt, Laura Martignon:
  On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?.
  
• Nicol N. Schraudolph, Douglas Aberdeen, Jin Yu:
  Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation.
  
• Brad Schumitsch, Sebastian Thrun, Gary R. Bradski, Kunle Olukotun:
  The Information-Form Data Association Filter.
  
• Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
  A Bayesian Framework for Tilt Perception and Confidence.
  
• Clayton Scott, Robert D. Nowak:
  Learning Minimum Volume Sets.
  
• Rafael Serrano-Gotarredona, Matthias Oster, Patrick Lichtsteiner, Alejandro Linares-Barranco, Rafael Paz-Vicente, Francisco Gomez-Rodriguez, Håvard Kolle Riis, Tobi Delbrück, Shih-Chii Liu, S. Zahnd, Adrian M. Whatley, Rodney J. Douglas, Philipp Häfliger, Gabriel Jiménez-Moreno, Antón Civit, Teresa Serrano-Gotarredona, Antonio Acosta-Jimenez, Bernabé Linares-Barranco:
  AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems.
  
• Yirong Shen, Andrew Y. Ng, Matthias Seeger:
  Fast Gaussian Process Regression using KD-Trees.
  
• Aaron P. Shon, Keith Grochow, Aaron Hertzmann, Rajesh P. N. Rao:
  Learning Shared Latent Structure for Image Synthesis and Robotic Imitation.
  
• Jorge Silva, Jorge S. Marques, João Miranda Lemos:
  Selecting Landmark Points for Sparse Manifold Learning.
  
• Cristian Sminchisescu, Atul Kanaujia, Zhiguo Li, Dimitris N. Metaxas:
  Conditional Visual Tracking in Kernel Space.
  
• Edward Snelson, Zoubin Ghahramani:
  Sparse Gaussian Processes using Pseudo-inputs.
  
• Le Song, Evian Gordon, Elly Gysels:
  Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface.
  
• Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer:
  A General and Efficient Multiple Kernel Learning Algorithm.
  
• Mark Steyvers, Scott D. Brown:
  Prediction and Change Detection.
  
• Alan Stocker, Eero P. Simoncelli:
  Sensory Adaptation within a Bayesian Framework for Perception.
  
• Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky:
  Describing Visual Scenes using Transformed Dirichlet Processes.
  
• Masashi Sugiyama:
  Active Learning for Misspecified Models.
  
• Richard S. Sutton, Eddie J. Rafols, Anna Koop:
  Temporal Abstraction in Temporal-difference Networks.
  
• Jun Suzuki, Hideki Isozaki:
  Sequence and Tree Kernels with Statistical Feature Mining.
  
• Brian Taba, Kwabena Boahen:
  Silicon growth cones map silicon retina.
  
• Minija Tamosiunaite, Bernd Porr, Florentin Wörgötter:
  Temporally changing synaptic plasticity.
  
• Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
  Structured Prediction via the Extragradient Method.
  
• Sebastian Thrun:
  Affine Structure From Sound.
  
• Jo-Anne Ting, Aaron D'Souza, Kenji Yamamoto, Toshinori Yoshioka, Donna L. Hoffman, Lauren Sergio, Shinji Kakei, John Kalaska, Mitsuo Kawato, Peter Strick, Stefan Schaal:
  Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares.
  
• Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari:
  Generalization error bounds for classifiers trained with interdependent data.
  
• Deepak Verma, Rajesh P. N. Rao:
  Goal-Based Imitation as Probabilistic Inference over Graphical Models.
  
• Jean-Philippe Vert, Robert E. Thurman, William Stafford Noble:
  Kernels for gene regulatory regions.
  
• Régis Vert, Jean-Philippe Vert:
  Consistency of one-class SVM and related algorithms.
  
• Paul A. Viola, John C. Platt, Cha Zhang:
  Multiple Instance Boosting for Object Detection.
  
• Martin J. Wainwright:
  Estimating the wrong Markov random field: Benefits in the computation-limited setting.
  
• Michael B. Wakin, Marco F. Duarte, Shriram Sarvotham, Dror Baron, Richard G. Baraniuk:
  Recovery of Jointly Sparse Signals from Few Random Projections.
  
• Jack M. Wang, David J. Fleet, Aaron Hertzmann:
  Gaussian Process Dynamical Models.
  
• Xuerui Wang, Natasha Mohanty, Andrew McCallum:
  Group and Topic Discovery from Relations and Their Attributes.
  
• Manfred K. Warmuth:
  A Bayes Rule for Density Matrices.
  
• Kazuho Watanabe, Sumio Watanabe:
  Variational Bayesian Stochastic Complexity of Mixture Models.
  
• Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul:
  Distance Metric Learning for Large Margin Nearest Neighbor Classification.
  
• Inna Weiner, Tomer Hertz, Israel Nelken, Daphna Weinshall:
  Analyzing Auditory Neurons by Learning Distance Functions.
  
• Gabriel Y. Weintraub, C. Lanier Benkard, Benjamin Van Roy:
  Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games.
  
• Bo Wen, Kwabena Boahen:
  Active Bidirectional Coupling in a Cochlear Chip.
  
• Jim Wielaard, Paul Sajda:
  Neural mechanisms of contrast dependent receptive field size in V1.
  
• Christopher K. I. Williams, John A. Quinn, Neil McIntosh:
  Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care.
  
• David P. Wipf, Bhaskar D. Rao:
  Comparing the Effects of Different Weight Distributions on Finding Sparse Representations.
  
• K. Y. Michael Wong, David Saad, Zhuo Gao:
  Message passing for task redistribution on sparse graphs.
  
• Frank Wood, Stefan Roth, Michael J. Black:
  Modeling Neural Population Spiking Activity with Gibbs Distributions.
  
• Shipeng Yu, Kai Yu, Volker Tresp:
  Soft Clustering on Graphs.
  
• Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani:
  Extracting Dynamical Structure Embedded in Neural Activity.
  
• Alan L. Yuille:
  Augmented Rescorla-Wagner and Maximum Likelihood Estimation.
  
• Gregory J. Zelinsky, Wei Zhang, Bing Yu, Xin Chen, Dimitris Samaras:
  The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search.
  
• Yungang Zhang, Changshui Zhang:
  Separation of Music Signals by Harmonic Structure Modeling.
  
• Jian Zhang, Zoubin Ghahramani, Yiming Yang:
  Learning Multiple Related Tasks using Latent Independent Component Analysis.
  
• Lei Zhang, Dimitris Samaras, Nelly Alia-Klein, Nora D. Volkow, Rita Z. Goldstein:
  Modeling Neuronal Interactivity using Dynamic Bayesian Networks.
  
• Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Deb Roy:
  Learning Influence among Interacting Markov Chains.
  
• Zhenyue Zhang, Hongyuan Zha:
  A Domain Decomposition Method for Fast Manifold Learning.
  
• Tong Zhang, Rie Kubota Ando:
  Analysis of Spectral Kernel Design based Semi-supervised Learning.
  
• Wei Zhang, Hyejin Yang, Dimitris Samaras, Gregory J. Zelinsky:
  A Computational Model of Eye Movements during Object Class Detection.
  
• Long Zhu, Alan L. Yuille:
  A Hierarchical Compositional System for Rapid Object Detection.
  
• Martin Zinkevich, Amy Greenwald, Michael L. Littman:
  Cyclic Equilibria in Markov Games.
  
• Laurent Zwald, Gilles Blanchard:
  On the Convergence of Eigenspaces in Kernel Principal Component Analysis.
  

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