NIPS 2006 Proceedings

Advances in Neural Information Processing Systems 19 (NIPS 2006)

The papers below appear in Advances in Neural Information Processing Systems 19 edited by B. Schölkopf and J.C. Platt and T. Hoffman.
They are proceedings from the conference, "Neural Information Processing Systems 2006."

An Application of Reinforcement Learning to Aerobatic Helicopter Flight Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng
Tighter PAC-Bayes Bounds Amiran Ambroladze, Emilio Parrado-hernández, John S. Shawe-taylor
Online Classification for Complex Problems Using Simultaneous Projections Yonatan Amit, Shai Shalev-shwartz, Yoram Singer
Learning on Graph with Laplacian Regularization Rie K. Ando, Tong Zhang
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets Jerónimo Arenas-garcía, Kaare B. Petersen, Lars K. Hansen
Multi-Task Feature Learning Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning Peter Auer, Ronald Ortner
Efficient Methods for Privacy Preserving Face Detection Shai Avidan, Moshe Butman
Active learning for misspecified generalized linear models Francis R. Bach
Subordinate class recognition using relational object models Aharon B. Hillel, Daphna Weinshall
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models David Barber, Silvia Chiappa
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems David Barber, Bertrand Mesot
Sample Complexity of Policy Search with Known Dynamics Peter L. Bartlett, Ambuj Tewari
AdaBoost is Consistent Peter L. Bartlett, Mikhail Traskin
A selective attention multi--chip system with dynamic synapses and spiking neurons Chiara Bartolozzi, Giacomo Indiveri
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks Alexis Battle, Gal Chechik, Daphne Koller
Convergence of Laplacian Eigenmaps Mikhail Belkin, Partha Niyogi
Analysis of Representations for Domain Adaptation Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira
An Approach to Bounded Rationality Eli Ben-sasson, Ehud Kalai, Adam Kalai
Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle
Dirichlet-Enhanced Spam Filtering based on Biased Samples Steffen Bickel, Tobias Scheffer
Detecting Humans via Their Pose Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto
Similarity by Composition Oren Boiman, Michal Irani
Denoising and Dimension Reduction in Feature Space Mikio L. Braun, Klaus-Robert Müller, Joachim M. Buhmann
Learning to Rank with Nonsmooth Cost Functions Christopher J. Burges, Robert Ragno, Quoc V. Le
Conditional mean field Peter Carbonetto, Nando D. Freitas
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation Gavin C. Cawley, Nicola L. Talbot, Mark Girolami
Branch and Bound for Semi-Supervised Support Vector Machines Olivier Chapelle, Vikas Sindhwani, S. S. Keerthi
Automated Hierarchy Discovery for Planning in Partially Observable Environments Laurent Charlin, Pascal Poupart, Romy Shioda
Max-margin classification of incomplete data Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
implicit Online Learning with Kernels Li Cheng, Dale Schuurmans, Shaojun Wang, Terry Caelli, S.v.n. Vishwanathan
Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons Elisabetta Chicca, Giacomo Indiveri, Rodney J. Douglas
Bayesian Ensemble Learning Hugh A. Chipman, Edward I. George, Robert E. Mcculloch
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions Christian Walder, Olivier Chapelle, Bernhard Schölkopf
Map-Reduce for Machine Learning on Multicore Cheng-tao Chu, Sang K. Kim, Yi-an Lin, Yuanyuan Yu, Gary Bradski, Kunle Olukotun, Andrew Y. Ng
Relational Learning with Gaussian Processes Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. S. Keerthi
Recursive Attribute Factoring David Cohn, Deepak Verma, Karl Pfleger
On Transductive Regression Corinna Cortes, Mehryar Mohri
Balanced Graph Matching Timothee Cour, Praveen Srinivasan, Jianbo Shi
Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman
Kernels on Structured Objects Through Nested Histograms Marco Cuturi, Kenji Fukumizu
Differential Entropic Clustering of Multivariate Gaussians Jason V. Davis, Inderjit S. Dhillon
Support Vector Machines on a Budget Ofer Dekel, Yoram Singer
A Theory of Retinal Population Coding Eizaburo Doi, Michael S. Lewicki
Learning to Traverse Image Manifolds Piotr Dollár, Vincent Rabaud, Serge J. Belongie
Using Combinatorial Optimization within Max-Product Belief Propagation Daniel Tarlow, Gal Elidan, Daphne Koller, John C. Duchi
Optimal Single-Class Classification Strategies Ran El-Yaniv, Mordechai Nisenson
A Small World Threshold for Economic Network Formation Eyal Even-dar, Michael Kearns
PG-means: learning the number of clusters in data Yu Feng, Greg Hamerly
Clustering Under Prior Knowledge with Application to Image Segmentation Dong S. Cheng, Vittorio Murino, Mário Figueiredo
Multi-dynamic Bayesian Networks Karim Filali, Jeff A. Bilmes
Image Retrieval and Classification Using Local Distance Functions Andrea Frome, Yoram Singer, Jitendra Malik
Multiple Instance Learning for Computer Aided Diagnosis Murat Dundar, Balaji Krishnapuram, R. B. Rao, Glenn M. Fung
Distributed Inference in Dynamical Systems Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, Mark A. Paskin
iLSTD: Eligibility Traces and Convergence Analysis Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton
A PAC-Bayes Risk Bound for General Loss Functions Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
Bayesian Policy Gradient Algorithms Mohammad Ghavamzadeh, Yaakov Engel
Data Integration for Classification Problems Employing Gaussian Process Priors Mark Girolami, Mingjun Zhong
Approximate inference using planar graph decomposition Amir Globerson, Tommi S. Jaakkola
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints Carla P. Gomes, Ashish Sabharwal, Bart Selman
No-regret Algorithms for Online Convex Programs Geoffrey J. Gordon
Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis Amit Gore, Shantanu Chakrabartty
Approximate Correspondences in High Dimensions Kristen Grauman, Trevor Darrell
A Kernel Method for the Two-Sample-Problem Arthur Gretton, Karsten M. Borgwardt, Malte Rasch, Bernhard Schölkopf, Alex J. Smola
Learning Nonparametric Models for Probabilistic Imitation David B. Grimes, Daniel R. Rashid, Rajesh P. Rao
Training Conditional Random Fields for Maximum Labelwise Accuracy Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou
Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces Moritz Grosse-wentrup, Klaus Gramann, Martin Buss
Graph-Based Visual Saliency Jonathan Harel, Christof Koch, Pietro Perona
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds Gloria Haro, Gregory Randall, Guillermo Sapiro
Manifold Denoising Matthias Hein, Markus Maier
TrueSkill™: A Bayesian Skill Rating System Ralf Herbrich, Tom Minka, Thore Graepel
Prediction on a Graph with a Perceptron Mark Herbster, Massimiliano Pontil
Geometric entropy minimization (GEM) for anomaly detection and localization Alfred O. Hero
Single Channel Speech Separation Using Factorial Dynamics John R. Hershey, Trausti Kristjansson, Steven Rennie, Peder A. Olsen
Correcting Sample Selection Bias by Unlabeled Data Jiayuan Huang, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf, Alex J. Smola
Sparse Representation for Signal Classification Ke Huang, Selin Aviyente
In-Network PCA and Anomaly Detection Ling Huang, Long Nguyen, Minos Garofalakis, Michael I. Jordan, Anthony Joseph, Nina Taft
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models Alexander T. Ihler, Padhraic Smyth
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm Robert Jenssen, Torbjørn Eltoft, Mark Girolami, Deniz Erdogmus
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models Mark Johnson, Thomas L. Griffiths, Sharon Goldwater
A Humanlike Predictor of Facial Attractiveness Amit Kagian, Gideon Dror, Tommer Leyvand, Daniel Cohen-or, Eytan Ruppin
Clustering appearance and shape by learning jigsaws Anitha Kannan, John Winn, Carsten Rother
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models S. S. Keerthi, Vikas Sindhwani, Olivier Chapelle
Combining causal and similarity-based reasoning Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum
A Nonparametric Approach to Bottom-Up Visual Saliency Wolf Kienzle, Felix A. Wichmann, Matthias O. Franz, Bernhard Schölkopf
Hierarchical Dirichlet Processes with Random Effects Seyoung Kim, Padhraic Smyth
An Information Theoretic Framework for Eukaryotic Gradient Sensing Joseph M. Kimmel, Richard M. Salter, Peter J. Thomas
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons Stefan Klampfl, Wolfgang Maass, Robert A. Legenstein
Predicting spike times from subthreshold dynamics of a neuron Ryota Kobayashi, Shigeru Shinomoto
Gaussian and Wishart Hyperkernels Risi Kondor, Tony Jebara
Causal inference in sensorimotor integration Konrad P. Körding, Joshua B. Tenenbaum
Multiple timescales and uncertainty in motor adaptation Konrad P. Körding, Joshua B. Tenenbaum, Reza Shadmehr
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach Matthias Krauledat, Michael Schröder, Benjamin Blankertz, Klaus-Robert Müller
Accelerated Variational Dirichlet Process Mixtures Kenichi Kurihara, Max Welling, Nikos A. Vlassis
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier
Inducing Metric Violations in Human Similarity Judgements Julian Laub, Klaus-Robert Müller, Felix A. Wichmann, Jakob H. Macke
Modelling transcriptional regulation using Gaussian Processes Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields Chi-hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner
Efficient sparse coding algorithms Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time Michael D. Lee, Ian G. Fuss, Daniel J. Navarro
Efficient Structure Learning of Markov Networks using L_1-Regularization Su-in Lee, Varun Ganapathi, Daphne Koller
Aggregating Classification Accuracy across Time: Application to Single Trial EEG Steven Lemm, Christin Schäfer, Gabriel Curio
Uncertainty, phase and oscillatory hippocampal recall Máté Lengyel, Peter Dayan
Blind Motion Deblurring Using Image Statistics Anat Levin
Speakers optimize information density through syntactic reduction T. F. Jaeger, Roger P. Levy
Real-time adaptive information-theoretic optimization of neurophysiology experiments Jeremy Lewi, Robert Butera, Liam Paninski
Ordinal Regression by Extended Binary Classification Ling Li, Hsuan-tien Lin
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data Ping Li, Kenneth W. Church, Trevor J. Hastie
Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space Wenye Li, Kin-hong Lee, Kwong-sak Leung
Learnability and the doubling dimension Yi Li, Philip M. Long
Emergence of conjunctive visual features by quadratic independent component analysis J.t. Lindgren, Aapo Hyvärinen
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
Analysis of Contour Motions Ce Liu, William T. Freeman, Edward H. Adelson
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions Philip M. Long, Rocco Servedio
Dynamic Foreground/Background Extraction from Images and Videos using Random Patches Le Lu, Gregory D. Hager
Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning Gediminas Lukšys, Jérémie Knüsel, Denis Sheynikhovich, Carmen Sandi, Wulfram Gerstner
Statistical Modeling of Images with Fields of Gaussian Scale Mixtures Siwei Lyu, Eero P. Simoncelli
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments Michael I. Mandel, Daniel P. Ellis, Tony Jebara
Isotonic Conditional Random Fields and Local Sentiment Flow Yi Mao, Guy Lebanon
Part-based Probabilistic Point Matching using Equivalence Constraints Graham Mcneill, Sethu Vijayakumar
Modeling Dyadic Data with Binary Latent Factors Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis
Fast Discriminative Visual Codebooks using Randomized Clustering Forests Frank Moosmann, Bill Triggs, Frederic Jurie
Context Effects in Category Learning: An Investigation of Four Probabilistic Models Michael C. Mozer, Michael Shettel, Michael P. Holmes
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games Chris Murray, Geoffrey J. Gordon
Non-rigid point set registration: Coherent Point Drift Andriy Myronenko, Xubo Song, Miguel Á. Carreira-Perpiñán
Fundamental Limitations of Spectral Clustering Boaz Nadler, Meirav Galun
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts Hariharan Narayanan, Mikhail Belkin, Partha Niyogi
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments Daniel J. Navarro, Thomas L. Griffiths
Temporal dynamics of information content carried by neurons in the primary visual cortex Danko Nikolić, Stefan Haeusler, Wolf Singer, Wolfgang Maass
Blind source separation for over-determined delayed mixtures Lars Omlor, Martin Giese
The Neurodynamics of Belief Propagation on Binary Markov Random Fields Thomas Ott, Ruedi Stoop
Handling Advertisements of Unknown Quality in Search Advertising Sandeep Pandey, Christopher Olston
Bayesian Model Scoring in Markov Random Fields Sridevi Parise, Max Welling
Game Theoretic Algorithms for Protein-DNA binding Luis Pérez-breva, Luis E. Ortiz, Chen-hsiang Yeang, Tommi S. Jaakkola
Bayesian Image Super-resolution, Continued Lyndsey C. Pickup, David P. Capel, Stephen J. Roberts, Andrew Zisserman
Parameter Expanded Variational Bayesian Methods Tommi S. Jaakkola, Yuan Qi
Inferring Network Structure from Co-Occurrences Michael G. Rabbat, Mário Figueiredo, Robert Nowak
Unsupervised Regression with Applications to Nonlinear System Identification Ali Rahimi, Ben Recht
Stability of K-Means Clustering Alexander Rakhlin, Andrea Caponnetto
Learning to parse images of articulated bodies Deva Ramanan
Efficient Learning of Sparse Representations with an Energy-Based Model Marc'aurelio Ranzato, Christopher Poultney, Sumit Chopra, Yann L. Cun
Learning to be Bayesian without Supervision Martin Raphan, Eero P. Simoncelli
Boosting Structured Prediction for Imitation Learning J. A. Bagnell, Joel Chestnutt, David M. Bradley, Nathan D. Ratliff
Large Scale Hidden Semi-Markov SVMs Gunnar Rätsch, Sören Sonnenburg
Natural Actor-Critic for Road Traffic Optimisation Silvia Richter, Douglas Aberdeen, Jin Yu
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees Konrad Rieck, Pavel Laskov, Sören Sonnenburg
Learning annotated hierarchies from relational data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds Benjamin I. Rubinstein, Peter L. Bartlett, J. H. Rubinstein
Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation Jason M. Samonds, Brian R. Potetz, Tai S. Lee
Robotic Grasping of Novel Objects Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng
Theory and Dynamics of Perceptual Bistability Paul R. Schrater, Rashmi Sundareswara
Fast Iterative Kernel PCA Nicol N. Schraudolph, Simon Günter, S.v.n. Vishwanathan
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods Matthias Seeger
Information Bottleneck for Non Co-Occurrence Data Yevgeny Seldin, Noam Slonim, Naftali Tishby
Large Margin Hidden Markov Models for Automatic Speech Recognition Fei Sha, Lawrence K. Saul
Nonlinear physically-based models for decoding motor-cortical population activity Gregory Shakhnarovich, Sung-phil Kim, Michael J. Black
Convex Repeated Games and Fenchel Duality Shai Shalev-shwartz, Yoram Singer
Recursive ICA Honghao Shan, Lingyun Zhang, Garrison W. Cottrell
Chained Boosting Christian R. Shelton, Wesley Huie, Kin F. Kan
A recipe for optimizing a time-histogram Hideaki Shimazaki, Shigeru Shinomoto
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype Tobias Sing, Niko Beerenwinkel
Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space Kyung-ah Sohn, Eric P. Xing
Learning Dense 3D Correspondence Florian Steinke, Volker Blanz, Bernhard Schölkopf
An Oracle Inequality for Clipped Regularized Risk Minimizers Ingo Steinwart, Don Hush, Clint Scovel
Learning Structural Equation Models for fMRI Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David Mcgonigle, Amos J. Storkey
Mixture Regression for Covariate Shift Masashi Sugiyama, Amos J. Storkey
Modeling Human Motion Using Binary Latent Variables Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation Yee W. Teh, David Newman, Max Welling
Towards a general independent subspace analysis Fabian J. Theis
Linearly-solvable Markov decision problems Emanuel Todorov
Logistic Regression for Single Trial EEG Classification Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Müller
Large Margin Component Analysis Lorenzo Torresani, Kuang-chih Lee
Learning Motion Style Synthesis from Perceptual Observations Lorenzo Torresani, Peggy Hackney, Christoph Bregler
Large-Scale Sparsified Manifold Regularization Ivor W. Tsang, James T. Kwok
Scalable Discriminative Learning for Natural Language Parsing and Translation Joseph Turian, Benjamin Wellington, I. D. Melamed
Generalized Maximum Margin Clustering and Unsupervised Kernel Learning Hamed Valizadegan, Rong Jin
A Complexity-Distortion Approach to Joint Pattern Alignment Andrea Vedaldi, Stefano Soatto
Online Clustering of Moving Hyperplanes René Vidal
Comparative Gene Prediction using Conditional Random Fields Jade P. Vinson, David Decaprio, Matthew D. Pearson, Stacey Luoma, James E. Galagan
Fast Computation of Graph Kernels Karsten M. Borgwardt, Nicol N. Schraudolph, S.v.n. Vishwanathan
Temporal Coding using the Response Properties of Spiking Neurons Thomas Voegtlin
High-Dimensional Graphical Model Selection Using \ell_1-Regularized Logistic Regression Martin J. Wainwright, John D. Lafferty, Pradeep K. Ravikumar
Attentional Processing on a Spike-Based VLSI Neural Network Yingxue Wang, Rodney J. Douglas, Shih-Chii Liu
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension Manfred K. Warmuth, Dima Kuzmin
Graph Laplacian Regularization for Large-Scale Semidefinite Programming Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul
A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo Oliver Williams
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization Rey Ramírez, Jason Palmer, Scott Makeig, Bhaskar D. Rao, David P. Wipf
Particle Filtering for Nonparametric Bayesian Matrix Factorization Frank Wood, Thomas L. Griffiths
A Scalable Machine Learning Approach to Go Lin Wu, Pierre F. Baldi
A Local Learning Approach for Clustering Mingrui Wu, Bernhard Schölkopf
The Robustness-Performance Tradeoff in Markov Decision Processes Huan Xu, Shie Mannor
Optimal Change-Detection and Spiking Neurons Angela J. Yu
Stochastic Relational Models for Discriminative Link Prediction Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu
Nonnegative Sparse PCA Ron Zass, Amnon Shashua
Doubly Stochastic Normalization for Spectral Clustering Ron Zass, Amnon Shashua
Simplifying Mixture Models through Function Approximation Kai Zhang, James T. Kwok
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms Xinhua Zhang, Wee S. Lee
MLLE: Modified Locally Linear Embedding Using Multiple Weights Zhenyue Zhang, Jing Wang
Learning with Hypergraphs: Clustering, Classification, and Embedding Denny Zhou, Jiayuan Huang, Bernhard Schölkopf
Multi-Instance Multi-Label Learning with Application to Scene Classification Zhi-hua Zhou, Min-ling Zhang
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing Yuanhao Chen, Long Zhu, Alan L. Yuille
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