Advances in Neural Information Processing Systems 20 (NIPS 2007)

• An Analysis of Convex Relaxations for MAP Estimation Pawan Mudigonda, Vladimir Kolmogorov, Philip Torr
• Random Features for Large-Scale Kernel Machines Ali Rahimi, Benjamin Recht
• Compressed Regression Shuheng Zhou, Larry Wasserman, John Lafferty
• Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains Andrea Lecchini-visintini, John Lygeros, Jan Maciejowski
• Predictive Matrix-Variate t Models Shenghuo Zhu, Kai Yu, Yihong Gong
• Loop Series and Bethe Variational Bounds in Attractive Graphical Models Alan Willsky, Erik Sudderth, Martin J. Wainwright
• Stable Dual Dynamic Programming Tao Wang, Michael Bowling, Dale Schuurmans, Daniel Lizotte
• FilterBoost: Regression and Classification on Large Datasets Joseph K. Bradley, Robert E. Schapire
• Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila
• Efficient Principled Learning of Thin Junction Trees Anton Chechetka, Carlos Guestrin
• Regret Minimization in Games with Incomplete Information Martin Zinkevich, Michael Johanson, Michael Bowling, Carmelo Piccione
• A Bayesian Model of Conditioned Perception Alan A. Stocker, Eero Simoncelli
• Scan Strategies for Meteorological Radars Victoria Manfredi, Jim Kurose
• The Tradeoffs of Large Scale Learning Léon Bottou, Olivier Bousquet
• Inferring Elapsed Time from Stochastic Neural Processes Misha Ahrens, Maneesh Sahani
• A learning framework for nearest neighbor search Lawrence Cayton, Sanjoy Dasgupta
• Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods Alessandro Lazaric, Marcello Restelli, Andrea Bonarini
• Ensemble Clustering using Semidefinite Programming Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu
• Theoretical Analysis of Heuristic Search Methods for Online POMDPs Stephane Ross, Joelle Pineau, Brahim Chaib-draa
• A Constraint Generation Approach to Learning Stable Linear Dynamical Systems Byron Boots, Geoffrey J. Gordon, Sajid Siddiqi
• An online Hebbian learning rule that performs Independent Component Analysis Claudia Clopath, André Longtin, Wulfram Gerstner
• Modeling Natural Sounds with Modulation Cascade Processes Richard Turner, Maneesh Sahani
• Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition Maryam Mahdaviani, Tanzeem Choudhury
• How SVMs can estimate quantiles and the median Andreas Christmann, Ingo Steinwart
• Random Projections for Manifold Learning Chinmay Hegde, Michael Wakin, Richard Baraniuk
• Hippocampal Contributions to Control: The Third Way Máté Lengyel, Peter Dayan
• Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing Yuanhao Chen, Long Zhu, Chenxi Lin, Hongjiang Zhang, Alan L. Yuille
• Convex Learning with Invariances Choon Teo, Amir Globerson, Sam Roweis, Alex Smola
• The Noisy-Logical Distribution and its Application to Causal Inference Hongjing Lu, Alan L. Yuille
• DIFFRAC: a discriminative and flexible framework for clustering Francis Bach, Zaïd Harchaoui
• Bundle Methods for Machine Learning Quoc Le, Alex Smola, S.v.n. Vishwanathan
• Catching Up Faster in Bayesian Model Selection and Model Averaging Tim Erven, Steven Rooij, Peter Grünwald
• Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime Carbonell
• Receptive Fields without Spike-Triggering Guenther Zeck, Matthias Bethge, Jakob H. Macke
• Robust Regression with Twinned Gaussian Processes Andrew Naish-guzman, Sean Holden
• New Outer Bounds on the Marginal Polytope David Sontag, Tommi Jaakkola
• Neural characterization in partially observed populations of spiking neurons Jonathan Pillow, Peter Latham
• Bayesian Agglomerative Clustering with Coalescents Yee Teh, Hal Daume III, Daniel M. Roy
• Distributed Inference for Latent Dirichlet Allocation David Newman, Padhraic Smyth, Max Welling, Arthur Asuncion
• Better than least squares: comparison of objective functions for estimating linear-nonlinear models Tatyana Sharpee
• Structured Learning with Approximate Inference Alex Kulesza, Fernando Pereira
• On Ranking in Survival Analysis: Bounds on the Concordance Index Harald Steck, Balaji Krishnapuram, Cary Dehing-oberije, Philippe Lambin, Vikas C. Raykar
• Competition Adds Complexity Judy Goldsmith, Martin Mundhenk
• Classification via Minimum Incremental Coding Length (MICL) John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-yeung Shum
• Kernel Measures of Conditional Dependence Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf
• Bayesian Policy Learning with Trans-Dimensional MCMC Matthew Hoffman, Arnaud Doucet, Nando Freitas, Ajay Jasra
• Temporal Difference Updating without a Learning Rate Marcus Hutter, Shane Legg
• Bayes-Adaptive POMDPs Stephane Ross, Brahim Chaib-draa, Joelle Pineau
• Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach José Hernández-lobato, Tjeerd Dijkstra, Tom Heskes
• Convex Clustering with Exemplar-Based Models Danial Lashkari, Polina Golland
• Learning Bounds for Domain Adaptation John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
• SpAM: Sparse Additive Models Han Liu, Larry Wasserman, John Lafferty, Pradeep Ravikumar
• Bayesian Inference for Spiking Neuron Models with a Sparsity Prior Sebastian Gerwinn, Matthias Bethge, Jakob H. Macke, Matthias Seeger
• Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks Alex Graves, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber, Santiago Fernández
• The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information John Langford, Tong Zhang
• Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes Geoffrey E. Hinton, Russ R. Salakhutdinov
• Kernels on Attributed Pointsets with Applications Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharya, K. Ramakrishnan
• Testing for Homogeneity with Kernel Fisher Discriminant Analysis Moulines Eric, Francis Bach, Zaïd Harchaoui
• Sparse deep belief net model for visual area V2 Honglak Lee, Chaitanya Ekanadham, Andrew Ng
• Second Order Bilinear Discriminant Analysis for single trial EEG analysis Christoforos Christoforou, Paul Sajda, Lucas Parra
• Convex Relaxations of Latent Variable Training Yuhong Guo, Dale Schuurmans
• A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo Giudice
• The discriminant center-surround hypothesis for bottom-up saliency Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos
• Statistical Analysis of Semi-Supervised Regression Larry Wasserman, John Lafferty
• Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion J. Kolter, Pieter Abbeel, Andrew Ng
• Colored Maximum Variance Unfolding Le Song, Arthur Gretton, Karsten Borgwardt, Alex Smola
• Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis Venkat Chandrasekaran, Alan Willsky, Jason Johnson
• Ultrafast Monte Carlo for Statistical Summations Charles Isbell, Michael Holmes, Alexander Gray
• Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
• People Tracking with the Laplacian Eigenmaps Latent Variable Model Zhengdong Lu, Cristian Sminchisescu, Miguel Carreira-Perpiñán
• The Distribution Family of Similarity Distances Gertjan Burghouts, Arnold Smeulders, Jan-mark Geusebroek
• Congruence between model and human attention reveals unique signatures of critical visual events Robert Peters, Laurent Itti
• Multi-task Gaussian Process Prediction Edwin V. Bonilla, Kian Chai, Christopher Williams
• Multi-Task Learning via Conic Programming Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
• Incremental Natural Actor-Critic Algorithms Shalabh Bhatnagar, Mohammad Ghavamzadeh, Mark Lee, Richard S. Sutton
• Collective Inference on Markov Models for Modeling Bird Migration M.a. Elmohamed, Dexter Kozen, Daniel R. Sheldon
• EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection Pierre Ferrez, José Millán
• Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Klaus-Robert Müller, Vadim Nikulin
• The Infinite Gamma-Poisson Feature Model Michalis Titsias
• A Unified Near-Optimal Estimator For Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Stable Random Projections Ping Li, Trevor Hastie
• Continuous Time Particle Filtering for fMRI Lawrence Murray, Amos J. Storkey
• Computing Robust Counter-Strategies Michael Johanson, Martin Zinkevich, Michael Bowling
• Random Sampling of States in Dynamic Programming Chris Atkeson, Benjamin Stephens
• Predicting human gaze using low-level saliency combined with face detection Moran Cerf, Jonathan Harel, Wolfgang Einhaeuser, Christof Koch
• Local Algorithms for Approximate Inference in Minor-Excluded Graphs Kyomin Jung, Devavrat Shah
• Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization XuanLong Nguyen, Martin J. Wainwright, Michael Jordan
• Learning with Tree-Averaged Densities and Distributions Sergey Kirshner
• Variational inference for Markov jump processes Manfred Opper, Guido Sanguinetti
• Expectation Maximization and Posterior Constraints Kuzman Ganchev, Ben Taskar, João Gama
• Anytime Induction of Cost-sensitive Trees Saher Esmeir, Shaul Markovitch
• Optimal ROC Curve for a Combination of Classifiers Marco Barreno, Alvaro Cardenas, J. D. Tygar
• Modeling homophily and stochastic equivalence in symmetric relational data Peter Hoff
• On Sparsity and Overcompleteness in Image Models Pietro Berkes, Richard Turner, Maneesh Sahani
• A Probabilistic Approach to Language Change Alexandre Bouchard-côté, Percy S. Liang, Dan Klein, Thomas Griffiths
• Learning the 2-D Topology of Images Nicolas Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
• A Bayesian LDA-based model for semi-supervised part-of-speech tagging Kristina Toutanova, Mark Johnson
• Cluster Stability for Finite Samples Ohad Shamir, Naftali Tishby
• Variational Inference for Diffusion Processes Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-taylor
• Augmented Functional Time Series Representation and Forecasting with Gaussian Processes Nicolas Chapados, Yoshua Bengio
• Sparse Overcomplete Latent Variable Decomposition of Counts Data Madhusudana Shashanka, Bhiksha Raj, Paris Smaragdis
• Modelling motion primitives and their timing in biologically executed movements Ben Williams, Marc Toussaint, Amos J. Storkey
• Subspace-Based Face Recognition in Analog VLSI Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa
• Efficient multiple hyperparameter learning for log-linear models Chuan-sheng Foo, Chuong B., Andrew Ng
• Discovering Weakly-Interacting Factors in a Complex Stochastic Process Charlie Frogner, Avi Pfeffer
• Stability Bounds for Non-i.i.d. Processes Mehryar Mohri, Afshin Rostamizadeh
• Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks Ben Carterette, Rosie Jones
• Efficient Bayesian Inference for Dynamically Changing Graphs Ozgur Sumer, Umut Acar, Alexander Ihler, Ramgopal Mettu
• Markov Chain Monte Carlo with People Adam Sanborn, Thomas Griffiths
• Estimating disparity with confidence from energy neurons Eric Tsang, Bertram Shi
• Locality and low-dimensions in the prediction of natural experience from fMRI Francois Meyer, Greg Stephens
• Configuration Estimates Improve Pedestrian Finding Duan Tran, David Forsyth
• A General Boosting Method and its Application to Learning Ranking Functions for Web Search Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun
• Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations Amir Globerson, Tommi Jaakkola
• GRIFT: A graphical model for inferring visual classification features from human data Michael Ross, Andrew Cohen
• An in-silico Neural Model of Dynamic Routing through Neuronal Coherence Devarajan Sridharan, Brian Percival, John Arthur, Kwabena A. Boahen
• A general agnostic active learning algorithm Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni
• Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons Lars Buesing, Wolfgang Maass
• Hidden Common Cause Relations in Relational Learning Ricardo Silva, Wei Chu, Zoubin Ghahramani
• Modeling image patches with a directed hierarchy of Markov random fields Simon Osindero, Geoffrey E. Hinton
• The Generalized FITC Approximation Andrew Naish-guzman, Sean Holden
• Cooled and Relaxed Survey Propagation for MRFs Hai Chieu, Wee Lee, Yee Teh
• A Spectral Regularization Framework for Multi-Task Structure Learning Andreas Argyriou, Massimiliano Pontil, Yiming Ying, Charles Micchelli
• CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation Luis E. Ortiz
• Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity Dejan Pecevski, Wolfgang Maass, Robert Legenstein
• A New View of Automatic Relevance Determination David Wipf, Srikantan Nagarajan
• Sequential Hypothesis Testing under Stochastic Deadlines Peter Frazier, Angela J. Yu
• Discriminative Log-Linear Grammars with Latent Variables Slav Petrov, Dan Klein
• HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation Bing Zhao, Eric Xing
• Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs Ambuj Tewari, Peter Bartlett
• TrueSkill Through Time: Revisiting the History of Chess Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
• Topmoumoute Online Natural Gradient Algorithm Nicolas Roux, Pierre-antoine Manzagol, Yoshua Bengio
• Learning the structure of manifolds using random projections Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma
• Learning Monotonic Transformations for Classification Andrew Howard, Tony Jebara
• Combined discriminative and generative articulated pose and non-rigid shape estimation Leonid Sigal, Alexandru Balan, Michael Black
• Multiple-Instance Active Learning Burr Settles, Mark Craven, Soumya Ray
• Semi-Supervised Multitask Learning Qiuhua Liu, Xuejun Liao, Lawrence Carin
• Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-jeacques Slotine, Rodney Douglas
• Mining Internet-Scale Software Repositories Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes, Pierre Baldi
• Optimal models of sound localization by barn owls Brian Fischer
• Discriminative K-means for Clustering Jieping Ye, Zheng Zhao, Mingrui Wu
• Heterogeneous Component Analysis Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii
• An Analysis of Inference with the Universum Olivier Chapelle, Alekh Agarwal, Fabian Sinz, Bernhard Schölkopf
• Exponential Family Predictive Representations of State David Wingate, Satinder Baveja
• One-Pass Boosting Zafer Barutcuoglu, Phil Long, Rocco Servedio
• The Value of Labeled and Unlabeled Examples when the Model is Imperfect Kaushik Sinha, Mikhail Belkin
• On higher-order perceptron algorithms Claudio Gentile, Fabio Vitale, Cristian Brotto
• Adaptive Online Gradient Descent Elad Hazan, Alexander Rakhlin, Peter Bartlett
• Fast Variational Inference for Large-scale Internet Diagnosis Emre Kiciman, David Maltz, John Platt
• Learning and using relational theories Charles Kemp, Noah Goodman, Joshua Tenenbaum
• The Infinite Markov Model Daichi Mochihashi, Eiichiro Sumita
• Retrieved context and the discovery of semantic structure Vinayak Rao, Marc Howard
• Active Preference Learning with Discrete Choice Data Brochu Eric, Nando Freitas, Abhijeet Ghosh
• Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms Dominik Endres, Mike Oram, Johannes Schindelin, Peter Foldiak
• Online Linear Regression and Its Application to Model-Based Reinforcement Learning Alexander Strehl, Michael Littman
• Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations M. Mahmud, Sylvian Ray
• McRank: Learning to Rank Using Multiple Classification and Gradient Boosting Ping Li, Qiang Wu, Christopher Burges
• A Randomized Algorithm for Large Scale Support Vector Learning Krishnan Kumar, Chiru Bhattacharya, Ramesh Hariharan
• Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Buenau, Motoaki Kawanabe
• The Price of Bandit Information for Online Optimization Varsha Dani, Sham M. Kakade, Thomas Hayes
• Iterative Non-linear Dimensionality Reduction with Manifold Sculpting Michael Gashler, Dan Ventura, Tony Martinez
• Support Vector Machine Classification with Indefinite Kernels Ronny Luss, Alexandre D'aspremont
• Learning with Transformation Invariant Kernels Christian Walder, Olivier Chapelle
• A probabilistic model for generating realistic lip movements from speech Gwenn Englebienne, Tim Cootes, Magnus Rattray
• Automatic Generation of Social Tags for Music Recommendation Douglas Eck, Paul Lamere, Thierry Bertin-mahieux, Stephen Green
• Learning to classify complex patterns using a VLSI network of spiking neurons Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi
• Efficient Convex Relaxation for Transductive Support Vector Machine Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu
• Message Passing for Max-weight Independent Set Sujay Sanghavi, Devavrat Shah, Alan Willsky
• Boosting the Area under the ROC Curve Phil Long, Rocco Servedio
• Sparse Feature Learning for Deep Belief Networks Marc'aurelio Ranzato, Y-lan Boureau, Yann Cun
• Receding Horizon Differential Dynamic Programming Yuval Tassa, Tom Erez, William Smart
• A Risk Minimization Principle for a Class of Parzen Estimators Kristiaan Pelckmans, Johan Suykens, Bart Moor
• Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey Kephart, David Levine, Freeman Rawson, Charles Lefurgy
• A Game-Theoretic Approach to Apprenticeship Learning Umar Syed, Robert E. Schapire
• Scene Segmentation with CRFs Learned from Partially Labeled Images Bill Triggs, Jakob Verbeek
• Measuring Neural Synchrony by Message Passing Justin Dauwels, François Vialatte, Tomasz Rutkowski, Andrzej Cichocki
• Discriminative Batch Mode Active Learning Yuhong Guo, Dale Schuurmans
• Comparing Bayesian models for multisensory cue combination without mandatory integration Ulrik Beierholm, Ladan Shams, Wei J., Konrad Koerding
• What makes some POMDP problems easy to approximate? Wee Lee, Nan Rong, David Hsu
• Boosting Algorithms for Maximizing the Soft Margin Gunnar Rätsch, Manfred K. K. Warmuth, Karen Glocer
• Gaussian Process Models for Link Analysis and Transfer Learning Kai Yu, Wei Chu
• Near-Maximum Entropy Models for Binary Neural Representations of Natural Images Matthias Bethge, Philipp Berens
• Privacy-Preserving Belief Propagation and Sampling Michael Kearns, Jinsong Tan, Jennifer Wortman
• Bayesian Co-Training Shipeng Yu, Balaji Krishnapuram, Harald Steck, R. Rao, Rómer Rosales
• Supervised Topic Models Jon Mcauliffe, David Blei
• A Kernel Statistical Test of Independence Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song, Bernhard Schölkopf, Alex Smola
• Discriminative Keyword Selection Using Support Vector Machines Fred Richardson, William Campbell
• Probabilistic Matrix Factorization Andriy Mnih, Russ R. Salakhutdinov
• Density Estimation under Independent Similarly Distributed Sampling Assumptions Tony Jebara, Yingbo Song, Kapil Thadani
• Efficient Inference for Distributions on Permutations Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas
• Fitted Q-iteration in continuous action-space MDPs András Antos, Csaba Szepesvári, Rémi Munos
• Blind channel identification for speech dereverberation using l1-norm sparse learning Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel Lee
• Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression Sennay Ghebreab, Arnold Smeulders, Pieter Adriaans
• Agreement-Based Learning Percy S. Liang, Dan Klein, Michael Jordan
• Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination Yiu Lam, Bertram Shi
• A Bayesian Framework for Cross-Situational Word-Learning Noah Goodman, Joshua Tenenbaum, Michael Black
• Parallelizing Support Vector Machines on Distributed Computers Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui, Edward Chang
• Object Recognition by Scene Alignment Bryan Russell, Antonio Torralba, Ce Liu, Rob Fergus, William Freeman
• A neural network implementing optimal state estimation based on dynamic spike train decoding Omer Bobrowski, Ron Meir, Shy Shoham, Yonina Eldar
• Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria Elad Hazan, Satyen Kale
• Catching Change-points with Lasso Céline Levy-leduc, Zaïd Harchaoui
• Feature Selection Methods for Improving Protein Structure Prediction with Rosetta Ben Blum, David Baker, Michael Jordan, Philip Bradley, Rhiju Das, David E. Kim
• Selecting Observations against Adversarial Objectives Andreas Krause, Brendan Mcmahan, Carlos Guestrin, Anupam Gupta
• Spatial Latent Dirichlet Allocation Xiaogang Wang, Eric Grimson
• Learning Visual Attributes Vittorio Ferrari, Andrew Zisserman
• Collapsed Variational Inference for HDP Yee Teh, Kenichi Kurihara, Max Welling
• Progressive mixture rules are deviation suboptimal Jean-yves Audibert
• Experience-Guided Search: A Theory of Attentional Control David Baldwin, Michael C. Mozer
• Hierarchical Penalization Marie Szafranski, Yves Grandvalet, Pierre Morizet-mahoudeaux
• Linear programming analysis of loopy belief propagation for weighted matching Sujay Sanghavi, Dmitry Malioutov, Alan Willsky
• Learning Horizontal Connections in a Sparse Coding Model of Natural Images Pierre Garrigues, Bruno Olshausen
• COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking Markus Weimer, Alexandros Karatzoglou, Quoc Le, Alex Smola
• Infinite State Bayes-Nets for Structured Domains Max Welling, Ian Porteous, Evgeniy Bart
• Regularized Boost for Semi-Supervised Learning Ke Chen, Shihai Wang
• Consistent Minimization of Clustering Objective Functions Ulrike Luxburg, Stefanie Jegelka, Michael Kaufmann, Sébastien Bubeck
• The rat as particle filter Aaron C. Courville, Nathaniel Daw
• Non-parametric Modeling of Partially Ranked Data Guy Lebanon, Yi Mao
• Multiple-Instance Pruning For Learning Efficient Cascade Detectors Cha Zhang, Paul Viola