Advances in Neural Information Processing Systems 20 (NIPS 2007)
The papers below appear in Advances in Neural Information Processing Systems 20 edited by J.C. Platt and D. Koller and Y. Singer and S.T. Roweis.They are proceedings from the conference, "Neural Information Processing Systems 2007."
- Inferring Elapsed Time from Stochastic Neural Processes Misha Ahrens, Maneesh Sahani
- Fitted Q-iteration in continuous action-space MDPs András Antos, Csaba Szepesvári, Rémi Munos
- Variational Inference for Diffusion Processes Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John S. Shawe-taylor
- A Spectral Regularization Framework for Multi-Task Structure Learning Andreas Argyriou, Massimiliano Pontil, Yiming Ying, Charles A. Micchelli
- Random Sampling of States in Dynamic Programming Chris Atkeson, Benjamin Stephens
- Progressive mixture rules are deviation suboptimal Jean-yves Audibert
- DIFFRAC: a discriminative and flexible framework for clustering Francis R. Bach, Zaïd Harchaoui
- Optimal ROC Curve for a Combination of Classifiers Marco Barreno, Alvaro Cardenas, J. D. Tygar
- Adaptive Online Gradient Descent Elad Hazan, Alexander Rakhlin, Peter L. Bartlett
- One-Pass Boosting Zafer Barutcuoglu, Phil Long, Rocco Servedio
- Comparing Bayesian models for multisensory cue combination without mandatory integration Ulrik Beierholm, Ladan Shams, Wei J. Ma, Konrad Koerding
- On Sparsity and Overcompleteness in Image Models Pietro Berkes, Richard Turner, Maneesh Sahani
- Near-Maximum Entropy Models for Binary Neural Representations of Natural Images Matthias Bethge, Philipp Berens
- Incremental Natural Actor-Critic Algorithms Shalabh Bhatnagar, Mohammad Ghavamzadeh, Mark Lee, Richard S. Sutton
- Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Klaus-Robert Müller, Vadim V. Nikulin
- Supervised Topic Models Jon D. Mcauliffe, David M. Blei
- Learning Bounds for Domain Adaptation John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
- Feature Selection Methods for Improving Protein Structure Prediction with Rosetta Ben Blum, David Baker, Michael I. Jordan, Philip Bradley, Rhiju Das, David E. Kim
- A neural network implementing optimal state estimation based on dynamic spike train decoding Omer Bobrowski, Ron Meir, Shy Shoham, Yonina Eldar
- Multi-task Gaussian Process Prediction Edwin V. Bonilla, Kian M. Chai, Christopher Williams
- The Tradeoffs of Large Scale Learning Olivier Bousquet, Léon Bottou
- A Probabilistic Approach to Language Change Alexandre Bouchard-côté, Percy S. Liang, Dan Klein, Thomas L. Griffiths
- Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila
- FilterBoost: Regression and Classification on Large Datasets Joseph K. Bradley, Robert E. Schapire
- Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons Lars Buesing, Wolfgang Maass
- The Distribution Family of Similarity Distances Gertjan Burghouts, Arnold Smeulders, Jan-mark Geusebroek
- Discriminative Keyword Selection Using Support Vector Machines Fred Richardson, William M. Campbell
- Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks Ben Carterette, Rosie Jones
- Subspace-Based Face Recognition in Analog VLSI Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa
- A learning framework for nearest neighbor search Lawrence Cayton, Sanjoy Dasgupta
- Predicting human gaze using low-level saliency combined with face detection Moran Cerf, Jonathan Harel, Wolfgang Einhaeuser, Christof Koch
- Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis Venkat Chandrasekaran, Alan S. Willsky, Jason K. Johnson
- Parallelizing Support Vector Machines on Distributed Computers Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui, Edward Y. Chang
- Augmented Functional Time Series Representation and Forecasting with Gaussian Processes Nicolas Chapados, Yoshua Bengio
- Efficient Principled Learning of Thin Junction Trees Anton Chechetka, Carlos Guestrin
- Regularized Boost for Semi-Supervised Learning Ke Chen, Shihai Wang
- 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
- Cooled and Relaxed Survey Propagation for MRFs Hai L. Chieu, Wee S. Lee, Yee W. Teh
- How SVMs can estimate quantiles and the median Andreas Christmann, Ingo Steinwart
- Second Order Bilinear Discriminant Analysis for single trial EEG analysis Christoforos Christoforou, Paul Sajda, Lucas C. Parra
- An online Hebbian learning rule that performs Independent Component Analysis Claudia Clopath, André Longtin, Wulfram Gerstner
- Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
- TrueSkill Through Time: Revisiting the History of Chess Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
- The Price of Bandit Information for Online Optimization Varsha Dani, Sham M. Kakade, Thomas P. Hayes
- A general agnostic active learning algorithm Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni
- Measuring Neural Synchrony by Message Passing Justin Dauwels, François Vialatte, Tomasz Rutkowski, Andrzej S. Cichocki
- The rat as particle filter Aaron C. Courville, Nathaniel D. Daw
- Efficient multiple hyperparameter learning for log-linear models Chuan-sheng Foo, Chuong B. Do, Andrew Y. Ng
- Automatic Generation of Social Tags for Music Recommendation Douglas Eck, Paul Lamere, Thierry Bertin-mahieux, Stephen Green
- Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms Dominik Endres, Mike Oram, Johannes Schindelin, Peter Foldiak
- A probabilistic model for generating realistic lip movements from speech Gwenn Englebienne, Tim Cootes, Magnus Rattray
- Active Preference Learning with Discrete Choice Data Brochu Eric, Nando D. Freitas, Abhijeet Ghosh
- Catching Up Faster in Bayesian Model Selection and Model Averaging Tim V. Erven, Steven D. Rooij, Peter Grünwald
- Anytime Induction of Cost-sensitive Trees Saher Esmeir, Shaul Markovitch
- Learning Visual Attributes Vittorio Ferrari, Andrew Zisserman
- EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection Pierre Ferrez, José Millán
- Optimal models of sound localization by barn owls Brian J. Fischer
- A Bayesian Framework for Cross-Situational Word-Learning Noah Goodman, Joshua B. Tenenbaum, Michael J. Black
- Sequential Hypothesis Testing under Stochastic Deadlines Peter Frazier, Angela J. Yu
- Learning the structure of manifolds using random projections Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma
- Discovering Weakly-Interacting Factors in a Complex Stochastic Process Charlie Frogner, Avi Pfeffer
- Kernel Measures of Conditional Dependence Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf
- The discriminant center-surround hypothesis for bottom-up saliency Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos
- Learning Horizontal Connections in a Sparse Coding Model of Natural Images Pierre Garrigues, Bruno A. Olshausen
- Iterative Non-linear Dimensionality Reduction with Manifold Sculpting Michael Gashler, Dan Ventura, Tony Martinez
- On higher-order perceptron algorithms Claudio Gentile, Fabio Vitale, Cristian Brotto
- Bayesian Inference for Spiking Neuron Models with a Sparsity Prior Sebastian Gerwinn, Matthias Bethge, Jakob H. Macke, Matthias Seeger
- Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression Sennay Ghebreab, Arnold Smeulders, Pieter Adriaans
- A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo D. Giudice
- Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations Amir Globerson, Tommi S. Jaakkola
- Competition Adds Complexity Judy Goldsmith, Martin Mundhenk
- Expectation Maximization and Posterior Constraints Kuzman Ganchev, Ben Taskar, João Gama
- Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks Alex Graves, Marcus Liwicki, Horst Bunke, Juergen Schmidhuber, Santiago Fernández
- A Kernel Statistical Test of Independence Arthur Gretton, Kenji Fukumizu, Choon H. Teo, Le Song, Bernhard Schölkopf, Alex J. Smola
- Discriminative Batch Mode Active Learning Yuhong Guo, Dale Schuurmans
- Convex Relaxations of Latent Variable Training Yuhong Guo, Dale Schuurmans
- Testing for Homogeneity with Kernel Fisher Discriminant Analysis Moulines Eric, Francis R. Bach, Zaïd Harchaoui
- Catching Change-points with Lasso Céline Levy-leduc, Zaïd Harchaoui
- Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria Elad Hazan, Satyen Kale
- Nearest-Neighbor-Based Active Learning for Rare Category Detection Jingrui He, Jaime G. Carbonell
- Random Projections for Manifold Learning Chinmay Hegde, Michael Wakin, Richard Baraniuk
- Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach José M. Hernández-lobato, Tjeerd Dijkstra, Tom Heskes
- Modeling homophily and stochastic equivalence in symmetric relational data Peter Hoff
- Bayesian Policy Learning with Trans-Dimensional MCMC Matthew Hoffman, Arnaud Doucet, Nando D. Freitas, Ajay Jasra
- Ultrafast Monte Carlo for Statistical Summations Charles L. Isbell, Michael P. Holmes, Alexander G. Gray
- Learning Monotonic Transformations for Classification Andrew Howard, Tony Jebara
- What makes some POMDP problems easy to approximate? Wee S. Lee, Nan Rong, David Hsu
- Efficient Inference for Distributions on Permutations Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas
- Temporal Difference Updating without a Learning Rate Marcus Hutter, Shane Legg
- Density Estimation under Independent Similarly Distributed Sampling Assumptions Tony Jebara, Yingbo Song, Kapil Thadani
- Computing Robust Counter-Strategies Michael Johanson, Martin Zinkevich, Michael Bowling
- Local Algorithms for Approximate Inference in Minor-Excluded Graphs Kyomin Jung, Devavrat Shah
- Multi-Task Learning via Conic Programming Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
- Privacy-Preserving Belief Propagation and Sampling Michael Kearns, Jinsong Tan, Jennifer Wortman
- Learning and using relational theories Charles Kemp, Noah Goodman, Joshua B. Tenenbaum
- Learning with Tree-Averaged Densities and Distributions Sergey Kirshner
- Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion J. Z. Kolter, Pieter Abbeel, Andrew Y. Ng
- Selecting Observations against Adversarial Objectives Andreas Krause, Brendan Mcmahan, Carlos Guestrin, Anupam Gupta
- Structured Learning with Approximate Inference Alex Kulesza, Fernando Pereira
- A Randomized Algorithm for Large Scale Support Vector Learning Krishnan Kumar, Chiru Bhattacharya, Ramesh Hariharan
- Statistical Analysis of Semi-Supervised Regression Larry Wasserman, John D. Lafferty
- Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination Yiu M. Lam, Bertram E. Shi
- The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information John Langford, Tong Zhang
- Convex Clustering with Exemplar-Based Models Danial Lashkari, Polina Golland
- Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods Alessandro Lazaric, Marcello Restelli, Andrea Bonarini
- Learning the 2-D Topology of Images Nicolas L. Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
- Topmoumoute Online Natural Gradient Algorithm Nicolas L. Roux, Pierre-antoine Manzagol, Yoshua Bengio
- Non-parametric Modeling of Partially Ranked Data Guy Lebanon, Yi Mao
- Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains Andrea Lecchini-visintini, John Lygeros, Jan Maciejowski
- Sparse deep belief net model for visual area V2 Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng
- Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity Dejan Pecevski, Wolfgang Maass, Robert A. Legenstein
- Hippocampal Contributions to Control: The Third Way Máté Lengyel, Peter Dayan
- McRank: Learning to Rank Using Multiple Classification and Gradient Boosting Ping Li, Qiang Wu, Christopher J. Burges
- A Unified Near-Optimal Estimator For Dimension Reduction in l_\alpha (0<\alpha\leq 2) Using Stable Random Projections Ping Li, Trevor J. Hastie
- Agreement-Based Learning Percy S. Liang, Dan Klein, Michael I. Jordan
- Blind channel identification for speech dereverberation using l1-norm sparse learning Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel D. Lee
- Mining Internet-Scale Software Repositories Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes, Pierre F. Baldi
- Semi-Supervised Multitask Learning Qiuhua Liu, Xuejun Liao, Lawrence Carin
- Boosting the Area under the ROC Curve Phil Long, Rocco Servedio
- Support Vector Machine Classification with Indefinite Kernels Ronny Luss, Alexandre D'aspremont
- Consistent Minimization of Clustering Objective Functions Ulrike V. Luxburg, Stefanie Jegelka, Michael Kaufmann, Sébastien Bubeck
- Receptive Fields without Spike-Triggering Guenther Zeck, Matthias Bethge, Jakob H. Macke
- Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition Maryam Mahdaviani, Tanzeem Choudhury
- Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations M. M. Mahmud, Sylvian Ray
- Scan Strategies for Meteorological Radars Victoria Manfredi, Jim Kurose
- Locality and low-dimensions in the prediction of natural experience from fMRI Francois Meyer, Greg Stephens
- Learning to classify complex patterns using a VLSI network of spiking neurons Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi
- The Infinite Markov Model Daichi Mochihashi, Eiichiro Sumita
- Stability Bounds for Non-i.i.d. Processes Mehryar Mohri, Afshin Rostamizadeh
- Experience-Guided Search: A Theory of Attentional Control David Baldwin, Michael C. Mozer
- An Analysis of Convex Relaxations for MAP Estimation Pawan Mudigonda, Vladimir Kolmogorov, Philip Torr
- Continuous Time Particle Filtering for fMRI Lawrence Murray, Amos J. Storkey
- The Generalized FITC Approximation Andrew Naish-guzman, Sean Holden
- Robust Regression with Twinned Gaussian Processes Andrew Naish-guzman, Sean Holden
- Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-jeacques Slotine, Rodney J. Douglas
- Distributed Inference for Latent Dirichlet Allocation David Newman, Padhraic Smyth, Max Welling, Arthur U. Asuncion
- Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization Long Nguyen, Martin J. Wainwright, Michael I. Jordan
- Heterogeneous Component Analysis Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii
- Variational inference for Markov jump processes Manfred Opper, Guido Sanguinetti
- CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation Luis E. Ortiz
- Modeling image patches with a directed hierarchy of Markov random fields Simon Osindero, Geoffrey E. Hinton
- Kernels on Attributed Pointsets with Applications Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharya, K. Ramakrishnan
- A Risk Minimization Principle for a Class of Parzen Estimators Kristiaan Pelckmans, Johan Suykens, Bart D. Moor
- Congruence between model and human attention reveals unique signatures of critical visual events Robert Peters, Laurent Itti
- Discriminative Log-Linear Grammars with Latent Variables Slav Petrov, Dan Klein
- Neural characterization in partially observed populations of spiking neurons Jonathan W. Pillow, Peter E. Latham
- Fast Variational Inference for Large-scale Internet Diagnosis Emre Kiciman, David Maltz, John C. Platt
- Random Features for Large-Scale Kernel Machines Ali Rahimi, Benjamin Recht
- Sparse Feature Learning for Deep Belief Networks Marc'aurelio Ranzato, Y-lan Boureau, Yann L. Cun
- Retrieved context and the discovery of semantic structure Vinayak Rao, Marc Howard
- SpAM: Sparse Additive Models Han Liu, Larry Wasserman, John D. Lafferty, Pradeep K. Ravikumar
- On Ranking in Survival Analysis: Bounds on the Concordance Index Harald Steck, Balaji Krishnapuram, Cary Dehing-oberije, Philippe Lambin, Vikas C. Raykar
- GRIFT: A graphical model for inferring visual classification features from human data Michael Ross, Andrew Cohen
- Bayes-Adaptive POMDPs Stephane Ross, Brahim Chaib-draa, Joelle Pineau
- Theoretical Analysis of Heuristic Search Methods for Online POMDPs Stephane Ross, Joelle Pineau, Brahim Chaib-draa
- Object Recognition by Scene Alignment Bryan Russell, Antonio Torralba, Ce Liu, Rob Fergus, William T. Freeman
- Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes Geoffrey E. Hinton, Ruslan R. Salakhutdinov
- Probabilistic Matrix Factorization Andriy Mnih, Ruslan R. Salakhutdinov
- Markov Chain Monte Carlo with People Adam Sanborn, Thomas L. Griffiths
- Linear programming analysis of loopy belief propagation for weighted matching Sujay Sanghavi, Dmitry Malioutov, Alan S. Willsky
- Message Passing for Max-weight Independent Set Sujay Sanghavi, Devavrat Shah, Alan S. Willsky
- Multiple-Instance Active Learning Burr Settles, Mark Craven, Soumya Ray
- Cluster Stability for Finite Samples Ohad Shamir, Naftali Tishby
- Better than least squares: comparison of objective functions for estimating linear-nonlinear models Tatyana Sharpee
- Sparse Overcomplete Latent Variable Decomposition of Counts Data Madhusudana Shashanka, Bhiksha Raj, Paris Smaragdis
- Collective Inference on Markov Models for Modeling Bird Migration M.a. S. Elmohamed, Dexter Kozen, Daniel R. Sheldon
- A Constraint Generation Approach to Learning Stable Linear Dynamical Systems Byron Boots, Geoffrey J. Gordon, Sajid M. Siddiqi
- Combined discriminative and generative articulated pose and non-rigid shape estimation Leonid Sigal, Alexandru Balan, Michael J. Black
- Hidden Common Cause Relations in Relational Learning Ricardo Silva, Wei Chu, Zoubin Ghahramani
- Ensemble Clustering using Semidefinite Programming Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu
- The Value of Labeled and Unlabeled Examples when the Model is Imperfect Kaushik Sinha, Mikhail Belkin
- An Analysis of Inference with the Universum Olivier Chapelle, Alekh Agarwal, Fabian H. Sinz, Bernhard Schölkopf
- Bundle Methods for Machine Learning Quoc V. Le, Alex J. Smola, S.v.n. Vishwanathan
- Colored Maximum Variance Unfolding Le Song, Arthur Gretton, Karsten M. Borgwardt, Alex J. Smola
- New Outer Bounds on the Marginal Polytope David Sontag, Tommi S. Jaakkola
- An in-silico Neural Model of Dynamic Routing through Neuronal Coherence Devarajan Sridharan, Brian Percival, John Arthur, Kwabena A. Boahen
- A Bayesian Model of Conditioned Perception Alan Stocker, Eero P. Simoncelli
- Online Linear Regression and Its Application to Model-Based Reinforcement Learning Alexander L. Strehl, Michael L. Littman
- Loop Series and Bethe Variational Bounds in Attractive Graphical Models Alan S. Willsky, Erik B. Sudderth, Martin J. Wainwright
- Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V. Buenau, Motoaki Kawanabe
- Efficient Bayesian Inference for Dynamically Changing Graphs Ozgur Sumer, Umut Acar, Alexander T. Ihler, Ramgopal R. Mettu
- A Game-Theoretic Approach to Apprenticeship Learning Umar Syed, Robert E. Schapire
- Hierarchical Penalization Marie Szafranski, Yves Grandvalet, Pierre Morizet-mahoudeaux
- Receding Horizon Differential Dynamic Programming Yuval Tassa, Tom Erez, William D. Smart
- Bayesian Agglomerative Clustering with Coalescents Yee W. Teh, Hal Daume III, Daniel M. Roy
- Collapsed Variational Inference for HDP Yee W. Teh, Kenichi Kurihara, Max Welling
- Convex Learning with Invariances Choon H. Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola
- 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
- Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs Ambuj Tewari, Peter L. Bartlett
- The Infinite Gamma-Poisson Feature Model Michalis K. Titsias
- A Bayesian LDA-based model for semi-supervised part-of-speech tagging Kristina Toutanova, Mark Johnson
- Configuration Estimates Improve Pedestrian Finding Duan Tran, David A. Forsyth
- Estimating disparity with confidence from energy neurons Eric K. Tsang, Bertram E. Shi
- Modeling Natural Sounds with Modulation Cascade Processes Richard Turner, Maneesh Sahani
- Scene Segmentation with CRFs Learned from Partially Labeled Images Bill Triggs, Jakob J. Verbeek
- Learning with Transformation Invariant Kernels Christian Walder, Olivier Chapelle
- Stable Dual Dynamic Programming Tao Wang, Michael Bowling, Dale Schuurmans, Daniel J. Lizotte
- Spatial Latent Dirichlet Allocation Xiaogang Wang, Eric Grimson
- Boosting Algorithms for Maximizing the Soft Margin Gunnar Rätsch, Manfred K. Warmuth, Karen A. Glocer
- COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola
- Infinite State Bayes-Nets for Structured Domains Max Welling, Ian Porteous, Evgeniy Bart
- Modelling motion primitives and their timing in biologically executed movements Ben Williams, Marc Toussaint, Amos J. Storkey
- Exponential Family Predictive Representations of State David Wingate, Satinder S. Baveja
- A New View of Automatic Relevance Determination David P. Wipf, Srikantan S. Nagarajan
- Classification via Minimum Incremental Coding Length (MICL) John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-yeung Shum
- Efficient Convex Relaxation for Transductive Support Vector Machine Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu
- Discriminative K-means for Clustering Jieping Ye, Zheng Zhao, Mingrui Wu
- Gaussian Process Models for Link Analysis and Transfer Learning Kai Yu, Wei Chu
- Bayesian Co-Training Shipeng Yu, Balaji Krishnapuram, Harald Steck, R. B. Rao, Rómer Rosales
- The Noisy-Logical Distribution and its Application to Causal Inference Hongjing Lu, Alan L. Yuille
- Multiple-Instance Pruning For Learning Efficient Cascade Detectors Cha Zhang, Paul A. Viola
- HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation Bing Zhao, Eric P. Xing
- 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
- People Tracking with the Laplacian Eigenmaps Latent Variable Model Zhengdong Lu, Cristian Sminchisescu, Miguel Á. Carreira-Perpiñán
- Compressed Regression Shuheng Zhou, Larry Wasserman, John D. Lafferty
- Predictive Matrix-Variate t Models Shenghuo Zhu, Kai Yu, Yihong Gong
- Regret Minimization in Games with Incomplete Information Martin Zinkevich, Michael Johanson, Michael Bowling, Carmelo Piccione