Advances in Neural Information Processing Systems 19

Proceedings of the 2006 Conference

The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists—interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.

Table of Contents

  1. Contents
  2. Preface
  3. NIPS Foundation
  4. Commitees
  5. Reviewers
  6. 1. An Application of Reinforcement Learning to Aerobatic Helicopter Flight, Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng
  7. 2. Tighter PAC-Bayes Bounds, Amiran Ambroladze, Emilio Parrado-Hernandez, John Shawe-Taylor
  8. 3. Online Classification for Complex Problems Using Simultaneous Projections, Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer
  9. 4. Learning on Graph with Laplacian Regularization, Rie Kubota Ando, Tong Zhang
  10. 5. Sparse Kernel Orthonormalized PLS for feature extraction in large data sets, Jeronimo Arenas-Garcia, Kaare Brandt Petersen, Lars Kai Hansen
  11. 6. Multi-Task Feature Learning, Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil
  12. 7. Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning, Peter Auer, Ronald Ortner
  13. 8. Efficient Methods for Privacy Preserving Face Detection, Shai Avidan, Moshe Butman
  14. 9. Active learning for misspecified generalized linear models, Francis R. Bach
  15. 10. Subordinate class recognition using relational object models, Aharon Bar Hillel, Daphna Weinshall
  16. 11. Unified Inference for Variational Bayesian Linear Gaussian State-Space Models, David Barber, Silvia Chiappa
  17. 12. A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems, David Barber, Bertrand Mesot
  18. 13. Sample Complexity of Policy Search with Known Dynamics, Peter L. Bartlett, Ambuj Tewari
  19. 14. AdaBoost is Consistent, Peter L. Bartlett, Mikhail Traskin
  20. 15. A selective attention multi-chip system with dynamic synapses and spiking neurons, Chiara Bartolozzi, Giacomo Indiveri
  21. 16. Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks, Alexis Battle, Gal Chechik, Daphne Koller
  22. 17. Convergence of Laplacian Eigenmaps, Mikhail Belkin, Partha Niyogi
  23. 18. Analysis of Representations for Domain Adaptation, Shai Ben- David, John Blitzer, Koby Crammer, Fernando Pereira
  24. 19. An Approach to Bounded Rationality, Eli Ben-Sasson, Adam Tauman Kalai, Ehud Kalai
  25. 20. Greedy Layer-Wise Training of Deep Networks, Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle
  26. 21. Dirichlet-Enhanced Spam Filtering based on Biased Samples, Steffen Bickel, Tobias Scheffer
  27. 22. Detecting Humans via Their Pose, Alessandro Bissacco, Ming- Hsuan Yang, Stefano Soatto
  28. 23. Similarity by Composition, Oren Boiman, Michal Irani
  29. 24. Denoising and Dimension Reduction in Feature Space, Mikio L. Braun, Joachim Buhmann, Klaus-Robert Muller
  30. 25. Learning to Rank with Nonsmooth Cost Functions, Christopher J.C. Burges, Robert Ragno, Quoc Viet Le
  31. 26. Conditional mean field, Peter Carbonetto, Nando de Freitas
  32. 27. Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation, Gavin C. Cawley, Nicola L.C. Talbot, Mark Girolami
  33. 28. Branch and Bound for Semi-Supervised Support Vector Machines, Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
  34. 29. Automated Hierarchy Discovery for Planning in Partially Observable Environments, Laurent Charlin, Pascal Poupart, Romy Shioda.
  35. 30. Max-margin classification of incomplete data, Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller
  36. 31. Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
  37. 32. Implicit Online Learning with Kernels, Li Cheng, S.V.N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli
  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
  39. 34. Bayesian Ensemble Learning, Hugh A. Chipman, Edward I. George, Robert E. McCulloch
  40. 35. Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions, Christian Walder, Bernhard Scholkopf, Olivier Chapelle
  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
  42. 37. Relational Learning with Gaussian Processes, Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi
  43. 38. Recursive Attribute Factoring, David Cohn, Deepak Verma, Karl Pfleger
  44. 39. On Transductive Regression, Corinna Cortes, Mehryar Mohri
  45. 40. Balanced Graph Matching, Timothee Cour, Praveen Srinivasan, Jianbo Shi
  46. 41. Learning from Multiple Sources, Koby Crammer, Michael Kearns, Jennifer Wortman
  47. 42. Kernels on Structured Objects Through Nested Histograms, Marco Cuturi, Kenji Fukumizu
  48. 43. Differential Entropic Clustering of Multivariate Gaussians, Jason V. Davis, Inderjit Dhillon
  49. 44. Support Vector Machines on a Budget, Ofer Dekel, Yoram Singer
  50. 45. A Theory of Retinal Population Coding, Eizaburo Doi, Michael S. Lewicki
  51. 46. Learning to Traverse Image Manifolds, Piotr Dollar, Vincent Rabaud, Serge Belongie
  52. 47. Using Combinatorial Optimization within Max-Product Belief Propagation, John Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller
  53. 48. Optimal Single-Class Classification Strategies, Ran El-Yaniv, Mordechai Nisenson
  54. 49. A Small World Threshold for Economic Network Formation, Eyal Even-Dar, Michael Kearns
  55. 50. PG-means: learning the number of clusters in data, Yu Feng, Greg Hamerly
  56. 51. Clustering Under Prior Knowledge with Application to Image Segmentation, Mario A.T. Figueiredo, Dong Seon Cheng, Vittorio Murino
  57. 52. Multi-dynamic Bayesian Networks, Karim Filali, Jeff A. Bilmes
  58. 53. Image Retrieval and Classification Using Local Distance Functions, Andrea Frome, Yoram Singer, Jitendra Malik
  59. 54. Multiple Instance Learning for Computer Aided Diagnosis, Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao
  60. 55. Distributed Inference in Dynamical Systems, Stanislav Funiak, Carlos Guestrin, Mark Paskin, Rahul Sukthankar
  61. 56. iLSTD: Eligibility Traces and Convergence Analysis, Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton
  62. 57. A PAC-Bayes Risk Bound for General Loss Functions, Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario Marchand
  63. 58. Bayesian Policy Gradient Algorithms, Mohammad Ghavamzadeh, Yaakov Engel
  64. 59. Data Integration for Classification Problems Employing Gaussian Process Priors, Mark Girolami, Mingjun Zhong
  65. 60. Approximate inference using planar graph decomposition, Amir Globerson, Tommi S. Jaakkola
  66. 61. Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints, Carla P. Gomes, Ashish Sabharwal, Bart Selman
  67. 62. No-regret Algorithms for Online Convex Programs, Geoffrey J. Gordon
  68. 63. Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis, Amit Gore, Shantanu Chakrabartty
  69. 64. Approximate Correspondences in High Dimensions, Kristen Grauman, Trevor Darrell
  70. 65. A Kernel Method for the Two-Sample-Problem, Arthur Gretton, Karsten M. Borgwardt, Malte Rasch, Bernhard Scholkopf, Alexander J. Smola
  71. 66. Learning Nonparametric Models for Probabilistic Imitation, David B. Grimes, Daniel R. Rashid, Rajesh P.N. Rao
  72. 67. Training Conditional Random Fields for Maximum Labelwise Accuracy, Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou
  73. 68. Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces, Moritz Grosse-Wentrup, Klaus Gramann, Martin Buss
  74. 69. Graph-Based Visual Saliency, Jonathan Harel, Christof Koch, Pietro Perona
  75. 70. Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds, Gloria Haro, Gregory Randall, Guillermo Sapiro
  76. 71. Manifold Denoising, Matthias Hein, Markus Maier
  77. 72. TrueSkillTM: A Bayesian Skill Rating System, Ralf Herbrich, Tom Minka, Thore Graepel
  78. 73. Prediction on a Graph with a Perceptron, Mark Herbster, Massimiliano Pontil
  79. 74. Geometric entropy minimization (GEM) for anomaly detection and localization, Alfred O. Hero, III
  80. 75. Single Channel Speech Separation Using Factorial Dynamics, John R. Hershey, Trausti Kristjansson, Steven Rennie, Peder A. Olsen
  81. 76. Correcting Sample Selection Bias by Unlabeled Data, Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Scholkopf
  82. 77. Sparse Representation for Signal Classification, Ke Huang, Selin Aviyente
  83. 78. In-Network PCA and Anomaly Detection, Ling Huang, XuanLong Nguyen, Minos Garofalakis, Michael I. Jordan, Anthony Joseph, Nina Taft
  84. 79. Learning Time-Intensity Profiles of Human Activity using Non- Parametric Bayesian Models, Alexander T. Ihler, Padhraic Smyth
  85. 80. Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm, Robert Jenssen, Torbjorn Eltoft, Mark Girolami, Deniz Erdogmus
  86. 81. Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models, Mark Johnson, Thomas L. Griffiths, Sharon Goldwater
  87. 82. A Humanlike Predictor of Facial Attractiveness, Amit Kagian, Gideon Dror, Tommer Leyvand, Daniel Cohen-Or, Eytan Ruppin
  88. 83. Clustering appearance and shape by learning jigsaws, Anitha Kannan, John Winn, Carsten Rother
  89. 84. A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems, Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida
  90. 85. An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models, S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle
  91. 86. Combining causal and similarity-based reasoning, Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum
  92. 87. A Nonparametric Approach to Bottom-Up Visual Saliency, Wolf Kienzle, Felix A. Wichmann, Bernhard Scholkopf, Matthias O. Franz
  93. 88. Hierarchical Dirichlet Processes with Random Effects, Seyoung Kim, Padhraic Smyth
  94. 89. An Information Theoretic Framework for Eukaryotic Gradient Sensing, Joseph M. Kimmel, Richard M. Salter, Peter J. Thomas
  95. 90. Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons, Stefan Klampfl, Robert Legenstein, Wolfgang Maass
  96. 91. Predicting spike times from subthreshold dynamics of a neuron, Ryota Kobayashi, Shigeru Shinomoto
  97. 92. Gaussian and Wishart Hyperkernels, Risi Kondor, Tony Jebara
  98. 93. Causal inference in sensorimotor integration, Konrad P. Kording, Joshua B. Tenenbaum
  99. 94. Multiple timescales and uncertainty in motor adaptation, Konrad P. Kording, Joshua B. Tenenbaum, Reza Shadmehr
  100. 95. Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach, Matthias Krauledat, Michael Schroder, Benjamin Blankertz, Klaus-Robert Muller
  101. 96. Accelerated Variational Dirichlet Process Mixtures, Kenichi Kurihara, Max Welling, Nikos Vlassis
  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
  103. 98. Inducing Metric Violations in Human Similarity Judgements, Julian Laub, Jakob Macke, Klaus-Robert Muller, Felix A. Wichmann.
  104. 99. Modelling transcriptional regulation using Gaussian Processes, Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray
  105. 100. Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields, Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner
  106. 101. Efficient sparse coding algorithms, Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng
  107. 102. A Bayesian Approach to Diffusion Models of Decision-Making and Response Time, Michael D. Lee, Ian G. Fuss, Daniel J. Navarro
  108. 103. Efficient Structure Learning of Markov Networks using L1- Regularization, Su-In Lee, Varun Ganapathi, Daphne Koller
  109. 104. Aggregating Classification Accuracy across Time: Application to Single Trial EEG, Steven Lemm, Christin Schafer, Gabriel Curio
  110. 105. Uncertainty, phase and oscillatory hippocampal recall, Mate Lengyel, Peter Dayan
  111. 106. Blind Motion Deblurring Using Image Statistics, Anat Levin
  112. 107. Speakers optimize information density through syntactic reduction, Roger Levy, T. Florian Jaeger
  113. 108. Real-time adaptive information-theoretic optimization of neurophysiology experiments, Jeremy Lewi, Robert Butera, Liam Paninski
  114. 109. Ordinal Regression by Extended Binary Classification, Ling Li, Hsuan-Tien Lin
  115. 110. Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data, Ping Li, Kenneth W. Church, Trevor J. Hastie
  116. 111. Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space, Wenye Li, Kin-Hong Lee, Kwong-Sak Leung
  117. 112. Learnability and the doubling dimension, Yi Li, Philip M. Long
  118. 113. Emergence of conjunctive visual features by quadratic independent component analysis, J.T. Lindgren, Aapo Hyvarinen
  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
  120. 115. Analysis of Contour Motions, Ce Liu, William T. Freeman, Edward H. Adelson
  121. 116. Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions, Philip M. Long, Rocco A. Servedio
  122. 117. Dynamic Foreground/Background Extraction from Images and Videos using Random Patches, Le Lu, Gregory Hager
  123. 118. Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning, Gediminas Luksys, Jeremie Knusel, Denis Sheynikhovich, Carmen Sandi, Wulfram Gerstner
  124. 119. Statistical Modeling of Images with Fields of Gaussian Scale Mixtures, Siwei Lyu, Eero P. Simoncelli
  125. 120. An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments, Michael I. Mandel, Daniel P.W. Ellis, Tony Jebara
  126. 121. Isotonic Conditional Random Fields and Local Sentiment Flow, Yi Mao, Guy Lebanon
  127. 122. Part-based Probabilistic Point Matching using Equivalence Constraints, Graham McNeill, Sethu Vijayakumar
  128. 123. Modeling Dyadic Data with Binary Latent Factors, Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis
  129. 124. Fast Discriminative Visual Codebooks using Randomized Clustering Forests, Frank Moosmann, Bill Triggs, Frederic Jurie
  130. 125. Context Effects in Category Learning: An Investigation of Four Probabilistic Models, Michael C. Mozer, Michael Jones, Michael Shettel
  131. 126. Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games, Chris Murray, Geoffrey J. Gordon
  132. 127. Non-rigid point set registration: Coherent Point Drift, Andriy Myronenko, Xubo Song, Miguel A. Carreira-Perpinan
  133. 128. Fundamental Limitations of Spectral Clustering, Boaz Nadler, Meirav Galun
  134. 129. On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts, Hariharan Narayanan, Mikhail Belkin, Partha Niyogi
  135. 130. A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments, Daniel J. Navarro, Thomas L. Griffiths
  136. 131. Temporal dynamics of information content carried by neurons in the primary visual cortex, Danko Nikolic, Stefan Haeusler, Wolf Singer, Wolfgang Maass
  137. 132. Blind source separation for over-determined delayed mixtures, Lars Omlor, Martin Giese
  138. 133. The Neurodynamics of Belief Propagation on Binary Markov Random Fields, Thomas Ott, Ruedi Stoop
  139. 134. Handling Advertisements of Unknown Quality in Search Advertising, Sandeep Pandey, Christopher Olston
  140. 135. Bayesian Model Scoring in Markov Random Fields, Sridevi Parise, Max Welling
  141. 136. Game Theoretic Algorithms for Protein-DNA binding, Luis Perez- Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi S. Jaakkola
  142. 137. Bayesian Image Super-resolution, Continued, Lyndsey C. Pickup, David P. Capel, Stephen J. Roberts, Andrew Zisserman
  143. 138. Parameter Expanded Variational Bayesian Methods, Yuan (Alan) Qi, Tommi S. Jaakkola
  144. 139. Inferring Network Structure from Co-Occurrences, Michael G. Rabbat, Mario A.T. Figueiredo, Robert D. Nowak
  145. 140. Unsupervised Regression with Applications to Nonlinear System Identification, Ali Rahimi, Ben Recht
  146. 141. Stability of K-Means Clustering, Alexander Rakhlin, Andrea Caponnetto
  147. 142. Learning to parse images of articulated bodies, Deva Ramanan
  148. 143. Efficient Learning of Sparse Representations with an Energy-Based Model, Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, Yann LeCun
  149. 144. Learning to be Bayesian without Supervision, Martin Raphan, Eero P. Simoncelli
  150. 145. Boosting Structured Prediction for Imitation Learning, Nathan Ratliff, David Bradley, J. Andrew Bagnell, Joel Chestnutt
  151. 146. Large Scale Hidden Semi-Markov SVMs, Gunnar Ratsch, Soren Sonnenburg
  152. 147. Natural Actor-Critic for Road Traffic Optimisation, Silvia Richter, Douglas Aberdeen, Jin Yu
  153. 148. Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees, Konrad Rieck, Pavel Laskov, Soren Sonnenburg.
  154. 149. Learning annotated hierarchies from relational data, Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum
  155. 150. Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds, Benjamin I.P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein
  156. 151. Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation, Jason M. Samonds, Brian R. Potetz, Tai Sing Lee
  157. 152. Robotic Grasping of Novel Objects, Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng
  158. 153. Theory and Dynamics of Perceptual Bistability, Paul R. Schrater, Rashmi Sundareswara
  159. 154. Fast Iterative Kernel PCA, Nicol N. Schraudolph, Simon Gunter, S.V.N. Vishwanathan
  160. 155. Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods, Matthias W. Seeger
  161. 156. Information Bottleneck for Non Co-Occurrence Data, Yevgeny Seldin, Noam Slonim, Naftali Tishby
  162. 157. Large Margin Hidden Markov Models for Automatic Speech Recognition, Fei Sha, Lawrence K. Saul
  163. 158. Nonlinear physically-based models for decoding motor-cortical population activity, Gregory Shakhnarovich, Sung-Phil Kim, Michael J. Black
  164. 159. Convex Repeated Games and Fenchel Duality, Shai Shalev- Shwartz, Yoram Singer
  165. 160. Recursive ICA, Honghao Shan, Lingyun Zhang, Garrison W. Cottrell
  166. 161. Chained Boosting, Christian R. Shelton, Wesley Huie, Kin Fai Kan
  167. 162. A recipe for optimizing a time-histogram, Hideaki Shimazaki, Shigeru Shinomoto
  168. 163. Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype, Tobias Sing, Niko Beerenwinkel
  169. 164. Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space, Kyung-Ah Sohn, Eric P. Xing
  170. 165. Learning Dense 3D Correspondence, Florian Steinke, Bernhard Scholkopf, Volker Blanz
  171. 166. An Oracle Inequality for Clipped Regularized Risk Minimizers, Ingo Steinwart, Don Hush, Clint Scovel
  172. 167. Learning Structural Equation Models for fMRI, Amos J. Storkey, Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David McGonigle
  173. 168. Mixture Regression for Covariate Shift, Amos J. Storkey, Masashi Sugiyama
  174. 169. Modeling Human Motion Using Binary Latent Variables, Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis
  175. 170. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation, Yee Whye Teh, David Newman, Max Welling
  176. 171. Towards a general independent subspace analysis, Fabian J. Theis
  177. 172. Linearly-solvable Markov decision problems, Emanuel Todorov
  178. 173. Logistic Regression for Single Trial EEG Classification, Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Muller
  179. 174. Large Margin Component Analysis, Lorenzo Torresani, Kuangchih Lee
  180. 175. Learning Motion Style Synthesis from Perceptual Observations, Lorenzo Torresani, Peggy Hackney, Christoph Bregler
  181. 176. Large-Scale Sparsified Manifold Regularization, Ivor W. Tsang, James T. Kwok
  182. 177. Scalable Discriminative Learning for Natural Language Parsing and Translation, Joseph Turian, Benjamin Wellington, I. Dan Melamed
  183. 178. Generalized Maximum Margin Clustering and Unsupervised Kernel Learning, Hamed Valizadegan, Rong Jin
  184. 179. A Complexity-Distortion Approach to Joint Pattern Alignment, Andrea Vedaldi, Stefano Soatto
  185. 180. Online Clustering of Moving Hyperplanes, Rene Vidal
  186. 181. Comparative Gene Prediction using Conditional Random Fields, Jade P. Vinson, David DeCaprio, Matthew D. Pearson, Stacey Luoma, James E. Galagan
  187. 182. Fast Computation of Graph Kernels, S.V.N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph
  188. 183. Temporal Coding using the Response Properties of Spiking Neurons, Thomas Voegtlin
  189. 184. High-Dimensional Graphical Model Selection Using 1-Regularized Logistic Regression, Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty
  190. 185. Attentional Processing on a Spike-Based VLSI Neural Network, Yingxue Wang, Rodney J. Douglas, Shih-Chii Liu
  191. 186. Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension, Manfred K. Warmuth, Dima Kuzmin
  192. 187. Graph Laplacian Regularization for Large-Scale Semidefinite Programming, Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul
  193. 188. A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo, Oliver Williams
  194. 189. Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization, David Wipf, Rey Ramırez, Jason Palmer, Scott Makeig, Bhaskar Rao
  195. 190. Particle Filtering for Nonparametric Bayesian Matrix Factorization, Frank Wood, Thomas L. Griffiths
  196. 191. A Scalable Machine Learning Approach to Go, Lin Wu, Pierre Baldi
  197. 192. A Local Learning Approach for Clustering, Mingrui Wu, Bernhard Scholkopf
  198. 193. The Robustness-Performance Tradeoff in Markov Decision Processes, Huan Xu, Shie Mannor
  199. 194. Optimal Change-Detection and Spiking Neurons, Angela J. Yu
  200. 195. Stochastic Relational Models for Discriminative Link Prediction, Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu
  201. 196. Nonnegative Sparse PCA, Ron Zass, Amnon Shashua
  202. 197. Doubly Stochastic Normalization for Spectral Clustering, Ron Zass, Amnon Shashua
  203. 198. Simplifying Mixture Models through Function Approximation, Kai Zhang, James T. Kwok
  204. 199. Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms, Xinhua Zhang, Wee Sun Lee
  205. 200. MLLE: Modified Locally Linear Embedding Using Multiple Weights, Zhenyue Zhang, Jing Wang
  206. 201. Learning with Hypergraphs: Clustering, Classification, and Embedding, Dengyong Zhou, Jiayuan Huang, Bernhard Scholkopf
  207. 202. Multi-Instance Multi-Label Learning with Application to Scene Classification, Zhi-Hua Zhou, Min-Ling Zhang
  208. 203. Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing, Long (Leo) Zhu, Yuanhao Chen, Alan Yuille
  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
  210. Subject Index
  211. Author Index