Table of Contents

1. Contents

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2. Preface

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3. NIPS Foundation

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4. Commitees

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5. Reviewers

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6. 1. An Application of Reinforcement Learning to Aerobatic Helicopter Flight, Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng

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7. 2. Tighter PAC-Bayes Bounds, Amiran Ambroladze, Emilio Parrado-Hernandez, John Shawe-Taylor

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8. 3. Online Classification for Complex Problems Using Simultaneous Projections, Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer

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9. 4. Learning on Graph with Laplacian Regularization, Rie Kubota Ando, Tong Zhang

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10. 5. Sparse Kernel Orthonormalized PLS for feature extraction in large data sets, Jeronimo Arenas-Garcia, Kaare Brandt Petersen, Lars Kai Hansen

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11. 6. Multi-Task Feature Learning, Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

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12. 7. Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning, Peter Auer, Ronald Ortner

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13. 8. Efficient Methods for Privacy Preserving Face Detection, Shai Avidan, Moshe Butman

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14. 9. Active learning for misspecified generalized linear models, Francis R. Bach

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15. 10. Subordinate class recognition using relational object models, Aharon Bar Hillel, Daphna Weinshall

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16. 11. Unified Inference for Variational Bayesian Linear Gaussian State-Space Models, David Barber, Silvia Chiappa

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17. 12. A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems, David Barber, Bertrand Mesot

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18. 13. Sample Complexity of Policy Search with Known Dynamics, Peter L. Bartlett, Ambuj Tewari

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19. 14. AdaBoost is Consistent, Peter L. Bartlett, Mikhail Traskin

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20. 15. A selective attention multi-chip system with dynamic synapses and spiking neurons, Chiara Bartolozzi, Giacomo Indiveri

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21. 16. Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks, Alexis Battle, Gal Chechik, Daphne Koller

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22. 17. Convergence of Laplacian Eigenmaps, Mikhail Belkin, Partha Niyogi

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23. 18. Analysis of Representations for Domain Adaptation, Shai Ben- David, John Blitzer, Koby Crammer, Fernando Pereira

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24. 19. An Approach to Bounded Rationality, Eli Ben-Sasson, Adam Tauman Kalai, Ehud Kalai

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25. 20. Greedy Layer-Wise Training of Deep Networks, Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle

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26. 21. Dirichlet-Enhanced Spam Filtering based on Biased Samples, Steffen Bickel, Tobias Scheffer

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27. 22. Detecting Humans via Their Pose, Alessandro Bissacco, Ming- Hsuan Yang, Stefano Soatto

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28. 23. Similarity by Composition, Oren Boiman, Michal Irani

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29. 24. Denoising and Dimension Reduction in Feature Space, Mikio L. Braun, Joachim Buhmann, Klaus-Robert Muller

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30. 25. Learning to Rank with Nonsmooth Cost Functions, Christopher J.C. Burges, Robert Ragno, Quoc Viet Le

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31. 26. Conditional mean field, Peter Carbonetto, Nando de Freitas

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32. 27. Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation, Gavin C. Cawley, Nicola L.C. Talbot, Mark Girolami

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33. 28. Branch and Bound for Semi-Supervised Support Vector Machines, Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi

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34. 29. Automated Hierarchy Discovery for Planning in Partially Observable Environments, Laurent Charlin, Pascal Poupart, Romy Shioda.

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35. 30. Max-margin classification of incomplete data, Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller

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36. 31. Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers

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37. 32. Implicit Online Learning with Kernels, Li Cheng, S.V.N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli

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38. 33. Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons, Elisabetta Chicca, Giacomo Indiveri, Rodney J. Douglas

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39. 34. Bayesian Ensemble Learning, Hugh A. Chipman, Edward I. George, Robert E. McCulloch

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40. 35. Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions, Christian Walder, Bernhard Scholkopf, Olivier Chapelle

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41. 36. Map-Reduce for Machine Learning on Multicore, Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Y. Ng, Kunle Olukotun

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42. 37. Relational Learning with Gaussian Processes, Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi

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43. 38. Recursive Attribute Factoring, David Cohn, Deepak Verma, Karl Pfleger

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44. 39. On Transductive Regression, Corinna Cortes, Mehryar Mohri

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45. 40. Balanced Graph Matching, Timothee Cour, Praveen Srinivasan, Jianbo Shi

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46. 41. Learning from Multiple Sources, Koby Crammer, Michael Kearns, Jennifer Wortman

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47. 42. Kernels on Structured Objects Through Nested Histograms, Marco Cuturi, Kenji Fukumizu

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48. 43. Differential Entropic Clustering of Multivariate Gaussians, Jason V. Davis, Inderjit Dhillon

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49. 44. Support Vector Machines on a Budget, Ofer Dekel, Yoram Singer

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50. 45. A Theory of Retinal Population Coding, Eizaburo Doi, Michael S. Lewicki

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51. 46. Learning to Traverse Image Manifolds, Piotr Dollar, Vincent Rabaud, Serge Belongie

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52. 47. Using Combinatorial Optimization within Max-Product Belief Propagation, John Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller

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53. 48. Optimal Single-Class Classification Strategies, Ran El-Yaniv, Mordechai Nisenson

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54. 49. A Small World Threshold for Economic Network Formation, Eyal Even-Dar, Michael Kearns

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55. 50. PG-means: learning the number of clusters in data, Yu Feng, Greg Hamerly

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56. 51. Clustering Under Prior Knowledge with Application to Image Segmentation, Mario A.T. Figueiredo, Dong Seon Cheng, Vittorio Murino

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57. 52. Multi-dynamic Bayesian Networks, Karim Filali, Jeff A. Bilmes

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58. 53. Image Retrieval and Classification Using Local Distance Functions, Andrea Frome, Yoram Singer, Jitendra Malik

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59. 54. Multiple Instance Learning for Computer Aided Diagnosis, Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao

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60. 55. Distributed Inference in Dynamical Systems, Stanislav Funiak, Carlos Guestrin, Mark Paskin, Rahul Sukthankar

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61. 56. iLSTD: Eligibility Traces and Convergence Analysis, Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton

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62. 57. A PAC-Bayes Risk Bound for General Loss Functions, Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario Marchand

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63. 58. Bayesian Policy Gradient Algorithms, Mohammad Ghavamzadeh, Yaakov Engel

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64. 59. Data Integration for Classification Problems Employing Gaussian Process Priors, Mark Girolami, Mingjun Zhong

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65. 60. Approximate inference using planar graph decomposition, Amir Globerson, Tommi S. Jaakkola

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66. 61. Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints, Carla P. Gomes, Ashish Sabharwal, Bart Selman

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67. 62. No-regret Algorithms for Online Convex Programs, Geoffrey J. Gordon

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68. 63. Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis, Amit Gore, Shantanu Chakrabartty

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69. 64. Approximate Correspondences in High Dimensions, Kristen Grauman, Trevor Darrell

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70. 65. A Kernel Method for the Two-Sample-Problem, Arthur Gretton, Karsten M. Borgwardt, Malte Rasch, Bernhard Scholkopf, Alexander J. Smola

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71. 66. Learning Nonparametric Models for Probabilistic Imitation, David B. Grimes, Daniel R. Rashid, Rajesh P.N. Rao

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72. 67. Training Conditional Random Fields for Maximum Labelwise Accuracy, Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou

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73. 68. Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces, Moritz Grosse-Wentrup, Klaus Gramann, Martin Buss

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74. 69. Graph-Based Visual Saliency, Jonathan Harel, Christof Koch, Pietro Perona

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75. 70. Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds, Gloria Haro, Gregory Randall, Guillermo Sapiro

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76. 71. Manifold Denoising, Matthias Hein, Markus Maier

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77. 72. TrueSkillTM: A Bayesian Skill Rating System, Ralf Herbrich, Tom Minka, Thore Graepel

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78. 73. Prediction on a Graph with a Perceptron, Mark Herbster, Massimiliano Pontil

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79. 74. Geometric entropy minimization (GEM) for anomaly detection and localization, Alfred O. Hero, III

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80. 75. Single Channel Speech Separation Using Factorial Dynamics, John R. Hershey, Trausti Kristjansson, Steven Rennie, Peder A. Olsen

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81. 76. Correcting Sample Selection Bias by Unlabeled Data, Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Scholkopf

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82. 77. Sparse Representation for Signal Classification, Ke Huang, Selin Aviyente

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83. 78. In-Network PCA and Anomaly Detection, Ling Huang, XuanLong Nguyen, Minos Garofalakis, Michael I. Jordan, Anthony Joseph, Nina Taft

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84. 79. Learning Time-Intensity Profiles of Human Activity using Non- Parametric Bayesian Models, Alexander T. Ihler, Padhraic Smyth

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85. 80. Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm, Robert Jenssen, Torbjorn Eltoft, Mark Girolami, Deniz Erdogmus

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86. 81. Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models, Mark Johnson, Thomas L. Griffiths, Sharon Goldwater

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87. 82. A Humanlike Predictor of Facial Attractiveness, Amit Kagian, Gideon Dror, Tommer Leyvand, Daniel Cohen-Or, Eytan Ruppin

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88. 83. Clustering appearance and shape by learning jigsaws, Anitha Kannan, John Winn, Carsten Rother

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89. 84. A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems, Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

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90. 85. An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models, S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle

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91. 86. Combining causal and similarity-based reasoning, Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum

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92. 87. A Nonparametric Approach to Bottom-Up Visual Saliency, Wolf Kienzle, Felix A. Wichmann, Bernhard Scholkopf, Matthias O. Franz

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93. 88. Hierarchical Dirichlet Processes with Random Effects, Seyoung Kim, Padhraic Smyth

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94. 89. An Information Theoretic Framework for Eukaryotic Gradient Sensing, Joseph M. Kimmel, Richard M. Salter, Peter J. Thomas

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95. 90. Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons, Stefan Klampfl, Robert Legenstein, Wolfgang Maass

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96. 91. Predicting spike times from subthreshold dynamics of a neuron, Ryota Kobayashi, Shigeru Shinomoto

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97. 92. Gaussian and Wishart Hyperkernels, Risi Kondor, Tony Jebara

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98. 93. Causal inference in sensorimotor integration, Konrad P. Kording, Joshua B. Tenenbaum

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99. 94. Multiple timescales and uncertainty in motor adaptation, Konrad P. Kording, Joshua B. Tenenbaum, Reza Shadmehr

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100. 95. Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach, Matthias Krauledat, Michael Schroder, Benjamin Blankertz, Klaus-Robert Muller

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101. 96. Accelerated Variational Dirichlet Process Mixtures, Kenichi Kurihara, Max Welling, Nikos Vlassis

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102. 97. PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier, Alexandre Lacasse, Francois Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier

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103. 98. Inducing Metric Violations in Human Similarity Judgements, Julian Laub, Jakob Macke, Klaus-Robert Muller, Felix A. Wichmann.

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104. 99. Modelling transcriptional regulation using Gaussian Processes, Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray

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105. 100. Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields, Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner

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106. 101. Efficient sparse coding algorithms, Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng

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107. 102. A Bayesian Approach to Diffusion Models of Decision-Making and Response Time, Michael D. Lee, Ian G. Fuss, Daniel J. Navarro

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108. 103. Efficient Structure Learning of Markov Networks using L1- Regularization, Su-In Lee, Varun Ganapathi, Daphne Koller

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109. 104. Aggregating Classification Accuracy across Time: Application to Single Trial EEG, Steven Lemm, Christin Schafer, Gabriel Curio

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110. 105. Uncertainty, phase and oscillatory hippocampal recall, Mate Lengyel, Peter Dayan

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111. 106. Blind Motion Deblurring Using Image Statistics, Anat Levin

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112. 107. Speakers optimize information density through syntactic reduction, Roger Levy, T. Florian Jaeger

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113. 108. Real-time adaptive information-theoretic optimization of neurophysiology experiments, Jeremy Lewi, Robert Butera, Liam Paninski

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114. 109. Ordinal Regression by Extended Binary Classification, Ling Li, Hsuan-Tien Lin

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115. 110. Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data, Ping Li, Kenneth W. Church, Trevor J. Hastie

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116. 111. Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space, Wenye Li, Kin-Hong Lee, Kwong-Sak Leung

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117. 112. Learnability and the doubling dimension, Yi Li, Philip M. Long

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118. 113. Emergence of conjunctive visual features by quadratic independent component analysis, J.T. Lindgren, Aapo Hyvarinen

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119. 114. Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure, Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin, Sean Cutler

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120. 115. Analysis of Contour Motions, Ce Liu, William T. Freeman, Edward H. Adelson

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121. 116. Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions, Philip M. Long, Rocco A. Servedio

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122. 117. Dynamic Foreground/Background Extraction from Images and Videos using Random Patches, Le Lu, Gregory Hager

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123. 118. Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning, Gediminas Luksys, Jeremie Knusel, Denis Sheynikhovich, Carmen Sandi, Wulfram Gerstner

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124. 119. Statistical Modeling of Images with Fields of Gaussian Scale Mixtures, Siwei Lyu, Eero P. Simoncelli

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125. 120. An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments, Michael I. Mandel, Daniel P.W. Ellis, Tony Jebara

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126. 121. Isotonic Conditional Random Fields and Local Sentiment Flow, Yi Mao, Guy Lebanon

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127. 122. Part-based Probabilistic Point Matching using Equivalence Constraints, Graham McNeill, Sethu Vijayakumar

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128. 123. Modeling Dyadic Data with Binary Latent Factors, Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis

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129. 124. Fast Discriminative Visual Codebooks using Randomized Clustering Forests, Frank Moosmann, Bill Triggs, Frederic Jurie

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130. 125. Context Effects in Category Learning: An Investigation of Four Probabilistic Models, Michael C. Mozer, Michael Jones, Michael Shettel

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131. 126. Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games, Chris Murray, Geoffrey J. Gordon

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132. 127. Non-rigid point set registration: Coherent Point Drift, Andriy Myronenko, Xubo Song, Miguel A. Carreira-Perpinan

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133. 128. Fundamental Limitations of Spectral Clustering, Boaz Nadler, Meirav Galun

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134. 129. On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts, Hariharan Narayanan, Mikhail Belkin, Partha Niyogi

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135. 130. A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments, Daniel J. Navarro, Thomas L. Griffiths

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136. 131. Temporal dynamics of information content carried by neurons in the primary visual cortex, Danko Nikolic, Stefan Haeusler, Wolf Singer, Wolfgang Maass

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137. 132. Blind source separation for over-determined delayed mixtures, Lars Omlor, Martin Giese

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138. 133. The Neurodynamics of Belief Propagation on Binary Markov Random Fields, Thomas Ott, Ruedi Stoop

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139. 134. Handling Advertisements of Unknown Quality in Search Advertising, Sandeep Pandey, Christopher Olston

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140. 135. Bayesian Model Scoring in Markov Random Fields, Sridevi Parise, Max Welling

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141. 136. Game Theoretic Algorithms for Protein-DNA binding, Luis Perez- Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi S. Jaakkola

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142. 137. Bayesian Image Super-resolution, Continued, Lyndsey C. Pickup, David P. Capel, Stephen J. Roberts, Andrew Zisserman

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143. 138. Parameter Expanded Variational Bayesian Methods, Yuan (Alan) Qi, Tommi S. Jaakkola

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144. 139. Inferring Network Structure from Co-Occurrences, Michael G. Rabbat, Mario A.T. Figueiredo, Robert D. Nowak

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145. 140. Unsupervised Regression with Applications to Nonlinear System Identification, Ali Rahimi, Ben Recht

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146. 141. Stability of K-Means Clustering, Alexander Rakhlin, Andrea Caponnetto

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147. 142. Learning to parse images of articulated bodies, Deva Ramanan

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148. 143. Efficient Learning of Sparse Representations with an Energy-Based Model, Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, Yann LeCun

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149. 144. Learning to be Bayesian without Supervision, Martin Raphan, Eero P. Simoncelli

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150. 145. Boosting Structured Prediction for Imitation Learning, Nathan Ratliff, David Bradley, J. Andrew Bagnell, Joel Chestnutt

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151. 146. Large Scale Hidden Semi-Markov SVMs, Gunnar Ratsch, Soren Sonnenburg

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152. 147. Natural Actor-Critic for Road Traffic Optimisation, Silvia Richter, Douglas Aberdeen, Jin Yu

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153. 148. Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees, Konrad Rieck, Pavel Laskov, Soren Sonnenburg.

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154. 149. Learning annotated hierarchies from relational data, Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum

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155. 150. Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds, Benjamin I.P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein

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156. 151. Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation, Jason M. Samonds, Brian R. Potetz, Tai Sing Lee

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157. 152. Robotic Grasping of Novel Objects, Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng

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158. 153. Theory and Dynamics of Perceptual Bistability, Paul R. Schrater, Rashmi Sundareswara

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159. 154. Fast Iterative Kernel PCA, Nicol N. Schraudolph, Simon Gunter, S.V.N. Vishwanathan

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160. 155. Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods, Matthias W. Seeger

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161. 156. Information Bottleneck for Non Co-Occurrence Data, Yevgeny Seldin, Noam Slonim, Naftali Tishby

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162. 157. Large Margin Hidden Markov Models for Automatic Speech Recognition, Fei Sha, Lawrence K. Saul

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163. 158. Nonlinear physically-based models for decoding motor-cortical population activity, Gregory Shakhnarovich, Sung-Phil Kim, Michael J. Black

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164. 159. Convex Repeated Games and Fenchel Duality, Shai Shalev- Shwartz, Yoram Singer

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165. 160. Recursive ICA, Honghao Shan, Lingyun Zhang, Garrison W. Cottrell

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166. 161. Chained Boosting, Christian R. Shelton, Wesley Huie, Kin Fai Kan

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167. 162. A recipe for optimizing a time-histogram, Hideaki Shimazaki, Shigeru Shinomoto

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168. 163. Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype, Tobias Sing, Niko Beerenwinkel

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169. 164. Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space, Kyung-Ah Sohn, Eric P. Xing

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170. 165. Learning Dense 3D Correspondence, Florian Steinke, Bernhard Scholkopf, Volker Blanz

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171. 166. An Oracle Inequality for Clipped Regularized Risk Minimizers, Ingo Steinwart, Don Hush, Clint Scovel

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172. 167. Learning Structural Equation Models for fMRI, Amos J. Storkey, Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David McGonigle

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173. 168. Mixture Regression for Covariate Shift, Amos J. Storkey, Masashi Sugiyama

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174. 169. Modeling Human Motion Using Binary Latent Variables, Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis

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175. 170. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation, Yee Whye Teh, David Newman, Max Welling

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176. 171. Towards a general independent subspace analysis, Fabian J. Theis

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177. 172. Linearly-solvable Markov decision problems, Emanuel Todorov

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178. 173. Logistic Regression for Single Trial EEG Classification, Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Muller

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179. 174. Large Margin Component Analysis, Lorenzo Torresani, Kuangchih Lee

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180. 175. Learning Motion Style Synthesis from Perceptual Observations, Lorenzo Torresani, Peggy Hackney, Christoph Bregler

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181. 176. Large-Scale Sparsified Manifold Regularization, Ivor W. Tsang, James T. Kwok

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182. 177. Scalable Discriminative Learning for Natural Language Parsing and Translation, Joseph Turian, Benjamin Wellington, I. Dan Melamed

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183. 178. Generalized Maximum Margin Clustering and Unsupervised Kernel Learning, Hamed Valizadegan, Rong Jin

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184. 179. A Complexity-Distortion Approach to Joint Pattern Alignment, Andrea Vedaldi, Stefano Soatto

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185. 180. Online Clustering of Moving Hyperplanes, Rene Vidal

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186. 181. Comparative Gene Prediction using Conditional Random Fields, Jade P. Vinson, David DeCaprio, Matthew D. Pearson, Stacey Luoma, James E. Galagan

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187. 182. Fast Computation of Graph Kernels, S.V.N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph

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188. 183. Temporal Coding using the Response Properties of Spiking Neurons, Thomas Voegtlin

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189. 184. High-Dimensional Graphical Model Selection Using 1-Regularized Logistic Regression, Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty

     PDF (584.8 KB)

190. 185. Attentional Processing on a Spike-Based VLSI Neural Network, Yingxue Wang, Rodney J. Douglas, Shih-Chii Liu

     PDF (762.5 KB)

191. 186. Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension, Manfred K. Warmuth, Dima Kuzmin

     PDF (630 KB)

192. 187. Graph Laplacian Regularization for Large-Scale Semidefinite Programming, Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul

     PDF (610.7 KB)

193. 188. A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo, Oliver Williams

     PDF (732.7 KB)

194. 189. Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization, David Wipf, Rey Ramırez, Jason Palmer, Scott Makeig, Bhaskar Rao

     PDF (574.9 KB)

195. 190. Particle Filtering for Nonparametric Bayesian Matrix Factorization, Frank Wood, Thomas L. Griffiths

     PDF (660.5 KB)

196. 191. A Scalable Machine Learning Approach to Go, Lin Wu, Pierre Baldi

     PDF (566.7 KB)

197. 192. A Local Learning Approach for Clustering, Mingrui Wu, Bernhard Scholkopf

     PDF (636.8 KB)

198. 193. The Robustness-Performance Tradeoff in Markov Decision Processes, Huan Xu, Shie Mannor

     PDF (590.9 KB)

199. 194. Optimal Change-Detection and Spiking Neurons, Angela J. Yu

     PDF (540.8 KB)

200. 195. Stochastic Relational Models for Discriminative Link Prediction, Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu

     PDF (629.1 KB)

201. 196. Nonnegative Sparse PCA, Ron Zass, Amnon Shashua

     PDF (592.8 KB)

202. 197. Doubly Stochastic Normalization for Spectral Clustering, Ron Zass, Amnon Shashua

     PDF (568 KB)

203. 198. Simplifying Mixture Models through Function Approximation, Kai Zhang, James T. Kwok

     PDF (709 KB)

204. 199. Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms, Xinhua Zhang, Wee Sun Lee

     PDF (650.4 KB)

205. 200. MLLE: Modified Locally Linear Embedding Using Multiple Weights, Zhenyue Zhang, Jing Wang

     PDF (1.1 MB)

206. 201. Learning with Hypergraphs: Clustering, Classification, and Embedding, Dengyong Zhou, Jiayuan Huang, Bernhard Scholkopf

     PDF (582 KB)

207. 202. Multi-Instance Multi-Label Learning with Application to Scene Classification, Zhi-Hua Zhou, Min-Ling Zhang

     PDF (551.8 KB)

208. 203. Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing, Long (Leo) Zhu, Yuanhao Chen, Alan Yuille

     PDF (741 KB)

209. 204. A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data, Johanna M. Zumer, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan

     PDF (1.6 MB)

210. Subject Index

     PDF (371.2 KB)

211. Author Index

     PDF (645.8 KB)