Book
Advances in Neural Information Processing Systems 20 (NIPS 2007)
Edited by:
J. Platt and D. Koller and Y. Singer and S. Roweis
- 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
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