NIPS 2007:
Vancouver,
British Columbia,
Canada
John C. Platt, Daphne Koller, Yoram Singer, Sam T. Roweis (Eds.):
Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007.
MIT Press 2008 BibTeX
- Misha Ahrens, Maneesh Sahani:
Inferring Elapsed Time from Stochastic Neural Processes.
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- András Antos, Rémi Munos, Csaba Szepesvári:
Fitted Q-iteration in continuous action-space MDPs.
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- Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
Variational Inference for Diffusion Processes.
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- Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
A Spectral Regularization Framework for Multi-Task Structure Learning.
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- Christopher G. Atkeson, Benjamin Stephens:
Random Sampling of States in Dynamic Programming.
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- Jean-Yves Audibert:
Progressive mixture rules are deviation suboptimal.
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- Francis Bach, Zaïd Harchaoui:
DIFFRAC: a discriminative and flexible framework for clustering.
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- Marco Barreno, Alvaro Cardenas, J. Doug Tygar:
Optimal ROC Curve for a Combination of Classifiers.
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- Peter L. Bartlett, Elad Hazan, Alexander Rakhlin:
Adaptive Online Gradient Descent.
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- Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio:
One-Pass Boosting.
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- Ulrik Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma:
Comparing Bayesian models for multisensory cue combination without mandatory integration.
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- Pietro Berkes, Richard Turner, Maneesh Sahani:
On Sparsity and Overcompleteness in Image Models.
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- Matthias Bethge, Philipp Berens:
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images.
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- Shalabh Bhatnagar, Richard Sutton, Mohammad Ghavamzadeh, Mark Lee:
Incremental Natural Actor-Critic Algorithms.
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- Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller:
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing.
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- David M. Blei, Jon D. McAuliffe:
Supervised Topic Models.
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- John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman:
Learning Bounds for Domain Adaptation.
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- Ben Blum, Michael I. Jordan, David Kim, Rhiju Das, Philip Bradley, David Baker:
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
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- Edwin V. Bonilla, Kian Ming Chai, Chris Williams:
Multi-task Gaussian Process Prediction.
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- Léon Bottou, Olivier Bousquet:
The Tradeoffs of Large Scale Learning.
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- Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein:
A Probabilistic Approach to Language Change.
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- Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila:
Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data.
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- Joseph K. Bradley, Robert E. Schapire:
FilterBoost: Regression and Classification on Large Datasets.
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- Lars Buesing, Wolfgang Maass:
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons.
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- Gertjan J. Burghouts, Arnold W. M. Smeulders, Jan-Mark Geusebroek:
The Distribution Family of Similarity Distances.
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- William Campbell, Fred Richardson:
Discriminative Keyword Selection Using Support Vector Machines.
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- Ben Carterette, Rosie Jones:
Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks.
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- Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa:
Subspace-Based Face Recognition in Analog VLSI.
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- Lawrence Cayton, Sanjoy Dasgupta:
A learning framework for nearest neighbor search.
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- Moran Cerf, Jonathan Harel, Wolfgang Einhäuser, Christof Koch:
Predicting human gaze using low-level saliency combined with face detection.
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- Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky:
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.
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- Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui:
Parallelizing Support Vector Machines on Distributed Computers.
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- Nicolas Chapados, Yoshua Bengio:
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes.
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- Anton Chechetka, Carlos Guestrin:
Efficient Principled Learning of Thin Junction Trees.
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- Ke Chen, Shihai Wang:
Regularized Boost for Semi-Supervised Learning.
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- Yuanhao Chen, Long Zhu, Chenxi Lin, Alan L. Yuille, HongJiang Zhang:
Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing.
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- Hai Leong Chieu, Wee Sun Lee, Yee Whye Teh:
Cooled and Relaxed Survey Propagation for MRFs.
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- Andreas Christmann, Ingo Steinwart:
How SVMs can estimate quantiles and the median.
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- Christoforos Christoforou, Paul Sajda, Lucas C. Parra:
Second Order Bilinear Discriminant Analysis for single trial EEG analysis.
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- Claudia Clopath, André Longtin, Wulfram Gerstner:
An online Hebbian learning rule that performs Independent Component Analysis.
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- John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.
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- Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess.
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- Varsha Dani, Thomas P. Hayes, Sham Kakade:
The Price of Bandit Information for Online Optimization.
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- Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni:
A general agnostic active learning algorithm.
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- Justin Dauwels, François B. Vialatte, Tomasz M. Rutkowski, Andrzej Cichocki:
Measuring Neural Synchrony by Message Passing.
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- Nathaniel Daw, Aaron Courville:
The rat as particle filter.
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- Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng:
Efficient multiple hyperparameter learning for log-linear models.
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- Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen Green:
Automatic Generation of Social Tags for Music Recommendation.
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- Dominik Endres, Mike W. Oram, Johannes Schindelin, Peter Földiák:
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
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- Gwenn Englebienne, Tim Cootes, Magnus Rattray:
A probabilistic model for generating realistic lip movements from speech.
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- Eric Brochu, Nando de Freitas, Abhijeet Ghosh:
Active Preference Learning with Discrete Choice Data.
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- Tim van Erven, Peter Grunwald, Steven de Rooij:
Catching Up Faster in Bayesian Model Selection and Model Averaging.
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- Saher Esmeir, Shaul Markovitch:
Anytime Induction of Cost-sensitive Trees.
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- Vittorio Ferrari, Andrew Zisserman:
Learning Visual Attributes.
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- Pierre W. Ferrez, José del R. Millán:
EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection.
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- Brian Fischer:
Optimal models of sound localization by barn owls.
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- Michael Frank, Noah Goodman, Joshua B. Tenenbaum:
A Bayesian Framework for Cross-Situational Word-Learning.
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- Peter Frazier, Angela Yu:
Sequential Hypothesis Testing under Stochastic Deadlines.
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- Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma:
Learning the structure of manifolds using random projections.
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- Charlie Frogner, Avi Pfeffer:
Discovering Weakly-Interacting Factors in a Complex Stochastic Process.
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- Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
Kernel Measures of Conditional Dependence.
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- Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos:
The discriminant center-surround hypothesis for bottom-up saliency.
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- Pierre Garrigues, Bruno Olshausen:
Learning Horizontal Connections in a Sparse Coding Model of Natural Images.
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- Michael Gashler, Dan Ventura, Tony R. Martinez:
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting.
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- Claudio Gentile, Fabio Vitale, Cristian Brotto:
On higher-order perceptron algorithms.
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- Sebastian Gerwinn, Jakob Macke, Matthias Seeger, Matthias Bethge:
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.
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- Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W. Adriaans:
Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression.
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- Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo Del Giudice:
A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses .
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- Amir Globerson, Tommi Jaakkola:
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
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- Judy Goldsmith, Martin Mundhenk:
Competition Adds Complexity.
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- João Graça, Kuzman Ganchev, Ben Taskar:
Expectation Maximization and Posterior Constraints.
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- Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber:
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks.
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- Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola:
A Kernel Statistical Test of Independence.
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- Yuhong Guo, Dale Schuurmans:
Discriminative Batch Mode Active Learning.
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- Yuhong Guo, Dale Schuurmans:
Convex Relaxations of Latent Variable Training.
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- Zaïd Harchaoui, Francis Bach, Eric Moulines:
Testing for Homogeneity with Kernel Fisher Discriminant Analysis.
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- Zaïd Harchaoui, Céline Lévy-Leduc:
Catching Change-points with Lasso.
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- Elad Hazan, Satyen Kale:
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria.
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- Jingrui He, Jaime G. Carbonell:
Nearest-Neighbor-Based Active Learning for Rare Category Detection.
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- Chinmay Hegde, Michael B. Wakin, Richard G. Baraniuk:
Random Projections for Manifold Learning.
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- José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes:
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
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- Peter Hoff:
Modeling homophily and stochastic equivalence in symmetric relational data.
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- Matthew Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
Bayesian Policy Learning with Trans-Dimensional MCMC.
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- Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.:
Ultrafast Monte Carlo for Statistical Summations.
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- Andrew Howard, Tony Jebara:
Learning Monotonic Transformations for Classification.
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- David Hsu, Wee Sun Lee, Nan Rong:
What makes some POMDP problems easy to approximate?
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- Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas:
Efficient Inference for Distributions on Permutations.
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- Marcus Hutter, Shane Legg:
Temporal Difference Updating without a Learning Rate.
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- Tony Jebara, Yingbo Song, Kapil Thadani:
Density Estimation under Independent Similarly Distributed Sampling Assumptions.
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- Michael Johanson, Martin Zinkevich, Michael H. Bowling:
Computing Robust Counter-Strategies.
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- Kyomin Jung, Devavrat Shah:
Local Algorithms for Approximate Inference in Minor-Excluded Graphs.
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- Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai:
Multi-Task Learning via Conic Programming.
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- Michael Kearns, Jinsong Tan, Jennifer Wortman:
Privacy-Preserving Belief Propagation and Sampling.
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- Charles Kemp, Noah Goodman, Joshua B. Tenenbaum:
Learning and using relational theories.
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- Sergey Kirshner:
Learning with Tree-Averaged Densities and Distributions.
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- J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng:
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
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- Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta:
Selecting Observations against Adversarial Objectives.
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- Alex Kulesza, Fernando Pereira:
Structured Learning with Approximate Inference.
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- Krishnan Kumar, Chiru Bhattacharyya, Ramesh Hariharan:
A Randomized Algorithm for Large Scale Support Vector Learning.
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- John D. Lafferty, Larry A. Wasserman:
Statistical Analysis of Semi-Supervised Regression.
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- Yiu Man Lam, Bertram Shi:
Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination.
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- John Langford, Tong Zhang:
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information.
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- Danial Lashkari, Polina Golland:
Convex Clustering with Exemplar-Based Models.
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- Alessandro Lazaric, Marcello Restelli, Andrea Bonarini:
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods.
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- Guy Lebanon, Yi Mao:
Non-parametric Modeling of Partially Ranked Data.
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- Andrea Lecchini-Visintini, John Lygeros, Jan Maciejowski:
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains.
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- Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng:
Sparse deep belief net model for visual area V2.
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- Robert A. Legenstein, Dejan Pecevski, Wolfgang Maass:
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
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- Máté Lengyel, Peter Dayan:
Hippocampal Contributions to Control: The Third Way.
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- Ping Li, Trevor Hastie:
A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections.
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- Ping Li, Christopher J. C. Burges, Qiang Wu:
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting.
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- Percy Liang, Dan Klein, Michael I. Jordan:
Agreement-Based Learning.
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- Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel Lee:
Blind channel identification for speech dereverberation using l1-norm sparse learning.
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- Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
Mining Internet-Scale Software Repositories.
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- Qiuhua Liu, Xuejun Liao, Lawrence Carin:
Semi-Supervised Multitask Learning.
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- Philip M. Long, Rocco A. Servedio:
Boosting the Area under the ROC Curve.
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- Zhengdong Lu, Miguel Á. Carreira-Perpiñán, Cristian Sminchisescu:
People Tracking with the Laplacian Eigenmaps Latent Variable Model.
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- Ronny Luss, Alexandre d'Aspremont:
Support Vector Machine Classification with Indefinite Kernels.
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- Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann:
Consistent Minimization of Clustering Objective Functions.
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- Jakob Macke, Guenther Zeck, Matthias Bethge:
Receptive Fields without Spike-Triggering.
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- Maryam Mahdaviani, Tanzeem Choudhury:
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition.
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- M. M. Mahmud, Sylvian R. Ray:
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations.
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- Victoria Manfredi, Jim Kurose:
Scan Strategies for Meteorological Radars.
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- Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar:
A neural network implementing optimal state estimation based on dynamic spike train decoding.
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- Francois Meyer, Greg Stephens:
Locality and low-dimensions in the prediction of natural experience from fMRI.
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- Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi:
Learning to classify complex patterns using a VLSI network of spiking neurons.
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- Daichi Mochihashi, Eiichiro Sumita:
The Infinite Markov Model.
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- Mehryar Mohri, Afshin Rostamizadeh:
Stability Bounds for Non-i.i.d. Processes.
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- Michael Mozer, David Baldwin:
Experience-Guided Search: A Theory of Attentional Control.
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- Pawan Mudigonda, Vladimir Kolmogorov, Philip H. S. Torr:
An Analysis of Convex Relaxations for MAP Estimation.
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- Lawrence Murray, Amos J. Storkey:
Continuous Time Particle Filtering for fMRI.
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- Andrew Naish-Guzman, Sean B. Holden:
Robust Regression with Twinned Gaussian Processes.
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- Andrew Naish-Guzman, Sean B. Holden:
The Generalized FITC Approximation.
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- Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-Jacques E. Slotine, Rodney J. Douglas:
Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons.
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- David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling:
Distributed Inference for Latent Dirichlet Allocation.
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- XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
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- Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii:
Heterogeneous Component Analysis.
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- Manfred Opper, Guido Sanguinetti:
Variational inference for Markov jump processes.
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- Luis E. Ortiz:
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation.
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- Simon Osindero, Geoffrey E. Hinton:
Modeling image patches with a directed hierarchy of Markov random fields.
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- Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharyya, K. R. Ramakrishnan:
Kernels on Attributed Pointsets with Applications.
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- Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Risk Minimization Principle for a Class of Parzen Estimators.
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- Robert J. Peters, Laurent Itti:
Congruence between model and human attention reveals unique signatures of critical visual events.
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- Slav Petrov, Dan Klein:
Discriminative Log-Linear Grammars with Latent Variables.
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- Jonathan Pillow, Peter E. Latham:
Neural characterization in partially observed populations of spiking neurons.
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- John C. Platt, Emre Kiciman, David A. Maltz:
Fast Variational Inference for Large-scale Internet Diagnosis.
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- Ali Rahimi, Benjamin Recht:
Random Features for Large-Scale Kernel Machines.
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- Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun:
Sparse Feature Learning for Deep Belief Networks.
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- Vinayak Rao, Marc Howard:
Retrieved context and the discovery of semantic structure.
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- Pradeep D. Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models.
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- Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin:
On Ranking in Survival Analysis: Bounds on the Concordance Index.
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- Michael Ross, Andrew Cohen:
GRIFT: A graphical model for inferring visual classification features from human data.
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- Stéphane Ross, Brahim Chaib-draa, Joelle Pineau:
Bayes-Adaptive POMDPs.
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- Stéphane Ross, Joelle Pineau, Brahim Chaib-draa:
Theoretical Analysis of Heuristic Search Methods for Online POMDPs.
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- Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl:
Learning the 2-D Topology of Images.
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- Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio:
Topmoumoute Online Natural Gradient Algorithm.
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- Bryan C. Russell, Antonio B. Torralba, Ce Liu, Robert Fergus, William T. Freeman:
Object Recognition by Scene Alignment.
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- Ruslan Salakhutdinov, Andriy Mnih:
Probabilistic Matrix Factorization.
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- Ruslan Salakhutdinov, Geoffrey E. Hinton:
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
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- Adam Sanborn, Thomas L. Griffiths:
Markov Chain Monte Carlo with People.
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- Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky:
Linear programming analysis of loopy belief propagation for weighted matching.
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- Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
Message Passing for Max-weight Independent Set.
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- Burr Settles, Mark Craven, Soumya Ray:
Multiple-Instance Active Learning.
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- Ohad Shamir, Naftali Tishby:
Cluster Stability for Finite Samples.
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- Tatyana Sharpee:
Better than least squares: comparison of objective functions for estimating linear-nonlinear models.
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- Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Smaragdis:
Sparse Overcomplete Latent Variable Decomposition of Counts Data.
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- Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen:
Collective Inference on Markov Models for Modeling Bird Migration.
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- Sajid Siddiqi, Byron Boots, Geoffrey Gordon:
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems.
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- Leonid Sigal, Alexandru O. Balan, Michael J. Black:
Combined discriminative and generative articulated pose and non-rigid shape estimation.
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- Ricardo Silva, Wei Chu, Zoubin Ghahramani:
Hidden Common Cause Relations in Relational Learning.
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- Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu:
Ensemble Clustering using Semidefinite Programming.
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- Kaushik Sinha, Mikhail Belkin:
The Value of Labeled and Unlabeled Examples when the Model is Imperfect.
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- Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf:
An Analysis of Inference with the Universum.
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- Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
Bundle Methods for Machine Learning.
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- Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton:
Colored Maximum Variance Unfolding.
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- David Sontag, Tommi Jaakkola:
New Outer Bounds on the Marginal Polytope.
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- Devarajan Sridharan, Brian Percival, John Arthur, Kwabena Boahen:
An in-silico Neural Model of Dynamic Routing through Neuronal Coherence.
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- Alan Stocker, Eero P. Simoncelli:
A Bayesian Model of Conditioned Perception.
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- Alexander L. Strehl, Michael L. Littman:
Online Linear Regression and Its Application to Model-Based Reinforcement Learning.
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- Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky:
Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
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- Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Von Bünau, Motoaki Kawanabe:
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.
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- Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu:
Efficient Bayesian Inference for Dynamically Changing Graphs.
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- Umar Syed, Robert E. Schapire:
A Game-Theoretic Approach to Apprenticeship Learning.
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- Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux:
Hierarchical Penalization.
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- Yuval Tassa, Tom Erez, William D. Smart:
Receding Horizon Differential Dynamic Programming.
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- Yee Whye Teh, Hal Daumé III, Daniel M. Roy:
Bayesian Agglomerative Clustering with Coalescents.
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- Yee Whye Teh, Kenichi Kurihara, Max Welling:
Collapsed Variational Inference for HDP.
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- Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola:
Convex Learning with Invariances.
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- Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart, David Levine, Freeman L. Rawson III, Charles Lefurgy:
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning.
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- Ambuj Tewari, Peter L. Bartlett:
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs.
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- Michalis K. Titsias:
The Infinite Gamma-Poisson Feature Model.
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- Kristina Toutanova, Mark Johnson:
A Bayesian LDA-based model for semi-supervised part-of-speech tagging.
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- Duan Tran, David Forsyth:
Configuration Estimates Improve Pedestrian Finding.
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- Eric K. C. Tsang, Bertram Emil Shi:
Estimating disparity with confidence from energy neurons.
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- Richard Turner, Maneesh Sahani:
Modeling Natural Sounds with Modulation Cascade Processes.
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- Jakob J. Verbeek, Bill Triggs:
Scene Segmentation with CRFs Learned from Partially Labeled Images.
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- Christian Walder, Olivier Chapelle:
Learning with Transformation Invariant Kernels.
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- Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Stable Dual Dynamic Programming.
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- Xiaogang Wang, Eric Grimson:
Spatial Latent Dirichlet Allocation.
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- Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin.
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- Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola:
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
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- Max Welling, Ian Porteous, Evgeniy Bart:
Infinite State Bayes-Nets for Structured Domains.
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- Ben Williams, Marc Toussaint, Amos J. Storkey:
Modelling motion primitives and their timing in biologically executed movements.
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- David Wingate, Satinder Singh Baveja:
Exponential Family Predictive Representations of State.
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- David P. Wipf, Srikantan Nagarajan:
A New View of Automatic Relevance Determination.
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- John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-Yeung Shum:
Classification via Minimum Incremental Coding Length (MICL).
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- Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu:
Efficient Convex Relaxation for Transductive Support Vector Machine.
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- Jieping Ye, Zheng Zhao, Mingrui Wu:
Discriminative K-means for Clustering.
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- Kai Yu, Wei Chu:
Gaussian Process Models for Link Analysis and Transfer Learning.
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- Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, Harald Steck, R. Bharat Rao:
Bayesian Co-Training.
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- Alan L. Yuille, Hongjing Lu:
The Noisy-Logical Distribution and its Application to Causal Inference.
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- Cha Zhang, Paul A. Viola:
Multiple-Instance Pruning For Learning Efficient Cascade Detectors.
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- Bing Zhao, Eric P. Xing:
HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation.
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- Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun:
A General Boosting Method and its Application to Learning Ranking Functions for Web Search.
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- Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression.
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- Shenghuo Zhu, Kai Yu, Yihong Gong:
Predictive Matrix-Variate t Models.
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- Martin Zinkevich, Michael Johanson, Michael H. Bowling, Carmelo Piccione:
Regret Minimization in Games with Incomplete Information.
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Copyright © Sat May 16 23:31:25 2009
by Michael Ley (ley@uni-trier.de)