Advances in Neural Information Processing Systems 19 (NIPS 2006)

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