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NIPS 2003: Vancouver, British Columbia, Canada
- Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf:
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press 2004, ISBN 0-262-20152-6 - Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky:
Efficient Multiscale Sampling from Products of Gaussian Mixtures. 1-8 - Mark A. Girolami, Ata Kabán:
Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. 9-16 - David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum:
Hierarchical Topic Models and the Nested Chinese Restaurant Process. 17-24 - Benjamin Taskar, Carlos Guestrin, Daphne Koller:
Max-Margin Markov Networks. 25-32 - Thore Graepel, Ralf Herbrich:
Invariant Pattern Recognition by Semi-Definite Programming Machines. 33-40 - Matthew Schultz, Thorsten Joachims:
Learning a Distance Metric from Relative Comparisons. 41-48 - Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani:
1-norm Support Vector Machines. 49-56 - Koji Tsuda, Gunnar Rätsch:
Image Reconstruction by Linear Programming. 57-64 - Stuart Andrews, Thomas Hofmann:
Multiple-Instance Learning via Disjunctive Programming Boosting. 65-72 - Tijl De Bie, Nello Cristianini:
Convex Methods for Transduction. 73-80 - Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Kernel Dimensionality Reduction for Supervised Learning. 81-88 - Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Clustering with the Connectivity Kernel. 89-96 - Haifeng Li, Tao Jiang, Keshu Zhang:
Efficient and Robust Feature Extraction by Maximum Margin Criterion. 97-104 - Thomas Strohmann, Andrei Belitski, Gregory Z. Grudic, Dennis DeCoste:
Sparse Greedy Minimax Probability Machine Classification. 105-112 - Jaco Vermaak, Simon J. Godsill, Arnaud Doucet:
Sequential Bayesian Kernel Regression. 113-120 - Claudio Gentile:
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms. 121-128 - Liva Ralaivola, Florence d'Alché-Buc:
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. 129-136 - Max Welling, Felix V. Agakov, Christopher K. I. Williams:
Extreme Components Analysis. 137-144 - Nathan Srebro, Tommi S. Jaakkola:
Linear Dependent Dimensionality Reduction. 145-152 - Xiaofei He, Partha Niyogi:
Locality Preserving Projections. 153-160 - Denis V. Chigirev, William Bialek:
Optimal Manifold Representation of Data: An Information Theoretic Approach. 161-168 - Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
Ranking on Data Manifolds. 169-176 - Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, Marie Ouimet:
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. 177-184 - Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss:
Pairwise Clustering and Graphical Models. 185-192 - Thomas P. Minka, Yuan (Alan) Qi:
Tree-structured Approximations by Expectation Propagation. 193-200 - David Barber, Felix V. Agakov:
Information Maximization in Noisy Channels : A Variational Approach. 201-208 - Eiji Mizutani, James Demmel:
Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply. 209-216 - Léon Bottou, Yann LeCun:
Large Scale Online Learning. 217-224 - Koby Crammer, Jaz S. Kandola, Yoram Singer:
Online Classification on a Budget. 225-232 - Xavier Carreras, Lluís Màrquez:
Online Learning via Global Feedback for Phrase Recognition. 233-240 - Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei L. Shishkin, Jianting Cao, Fanji Gu:
Sparse Representation and Its Applications in Blind Source Separation. 241-248 - David P. Wipf, Jason A. Palmer, Bhaskar D. Rao:
Perspectives on Sparse Bayesian Learning. 249-256 - Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum:
Semi-Supervised Learning with Trees. 257-264 - Ting Liu, Andrew W. Moore, Alexander G. Gray:
New Algorithms for Efficient High Dimensional Non-parametric Classification. 265-272 - Christopher J. Paciorek, Mark J. Schervish:
Nonstationary Covariance Functions for Gaussian Process Regression. 273-280 - Greg Hamerly, Charles Elkan:
Learning the k in k-means. 281-288 - Chen Yanover, Yair Weiss:
Finding the M Most Probable Configurations in Arbitrary Graphical Models. 289-296 - Jakob J. Verbeek, Sam T. Roweis, Nikos Vlassis:
Non-linear CCA and PCA by Alignment of Local Models. 297-304 - Francis R. Bach, Michael I. Jordan:
Learning Spectral Clustering. 305-312 - Corinna Cortes, Mehryar Mohri:
AUC Optimization vs. Error Rate Minimization. 313-320 - Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf:
Learning with Local and Global Consistency. 321-328 - Neil D. Lawrence:
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. 329-336 - Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani:
Warped Gaussian Processes. 337-344 - Allan Borodin, Ran El-Yaniv, Vincent Gogan:
Can We Learn to Beat the Best Stock. 345-352 - Tom Heskes, Onno Zoeter, Wim Wiegerinck:
Approximate Expectation Maximization. 353-360 - Max Welling, Yee Whye Teh:
Linear Response for Approximate Inference. 361-368 - Martin J. Wainwright, Michael I. Jordan:
Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. 369-376 - Alina Beygelzimer, Irina Rish:
Approximability of Probability Distributions. 377-384 - Quaid Morris, Brendan J. Frey:
Denoising and Untangling Graphs Using Degree Priors. 385-392 - XuanLong Nguyen, Michael I. Jordan:
On the Concentration of Expectation and Approximate Inference in Layered Networks. 393-400 - Radford M. Neal, Matthew J. Beal, Sam T. Roweis:
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models. 401-408 - Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Jon M. Kleinberg:
Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis. 409-416 - Geoffrey E. Hinton, Max Welling, Andriy Mnih:
Wormholes Improve Contrastive Divergence. 417-424 - Mark A. Paskin:
Sample Propagation. 425-432 - Amos J. Storkey:
Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data. 433-440 - Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin:
Laplace Propagation. 441-448 - Gökhan H. Bakir, Jason Weston, Bernhard Schölkopf:
Learning to Find Pre-Images. 449-456 - Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning. 457-464 - Noam Shental, Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall:
Computing Gaussian Mixture Models with EM Using Equivalence Constraints. 465-472 - Volker Roth, Tilman Lange:
Feature Selection in Clustering Problems. 473-480 - David Kauchak, Sanjoy Dasgupta:
An Iterative Improvement Procedure for Hierarchical Clustering. 481-488 - Zvika Marx, Ido Dagan, Eli Shamir:
Identifying Structure across Pre-partitioned Data. 489-496 - Ofer Dekel, Christopher D. Manning, Yoram Singer:
Log-Linear Models for Label Ranking. 497-504 - Matthew Brand:
Minimax Embeddings. 505-512 - Yoshua Bengio, Yves Grandvalet:
No Unbiased Estimator of the Variance of K-Fold Cross-Validation. 513-520 - Harald Steck, Tommi S. Jaakkola:
Bias-Corrected Bootstrap and Model Uncertainty. 521-528 - Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng:
Probability Estimates for Multi-Class Classification by Pairwise Coupling. 529-536 - Gang Ji, Jeff A. Bilmes:
Necessary Intransitive Likelihood-Ratio Classifiers. 537-544 - Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum:
Classification with Hybrid Generative/Discriminative Models. 545-552 - Victor Lavrenko, R. Manmatha, Jiwoon Jeon:
A Model for Learning the Semantics of Pictures. 553-560 - Michael J. Kearns, Luis E. Ortiz:
Algorithms for Interdependent Security Games. 561-568 - John C. Platt:
Fast Embedding of Sparse Similarity Graphs. 571-578 - Anton Schwaighofer, Marian Grigoras, Volker Tresp, Clemens Hoffmann:
GPPS: A Gaussian Process Positioning System for Cellular Networks. 579-586 - David I. Ferguson, Aaron Morris, Dirk Hähnel, Christopher R. Baker, Zachary Omohundro, Carlos F. Reverte, Scott Thayer, Charles Whittaker, William Whittaker, Wolfram Burgard, Sebastian Thrun:
An Autonomous Robotic System for Mapping Abandoned Mines. 587-594 - Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble:
Semi-supervised Protein Classification Using Cluster Kernels. 595-602 - Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alex Aiken:
Statistical Debugging of Sampled Programs. 603-610 - Nicholas P. Hughes, Lionel Tarassenko, Stephen J. Roberts:
Markov Models for Automated ECG Interval Analysis. 611-618 - Cynthia Archer, Todd K. Leen, António M. Baptista:
Parameterized Novelty Detectors for Environmental Sensor Monitoring. 619-626 - Benjamin M. Marlin:
Modeling User Rating Profiles For Collaborative Filtering. 627-634 - Michael J. Quinlan, Stephan K. Chalup, Richard H. Middleton:
Application of SVMs for Colour Classification and Collision Detection with AIBO Robots. 635-642 - Jun Suzuki, Yutaka Sasaki, Eisaku Maeda:
Kernels for Structured Natural Language Data. 643-650 - Daniel B. Neill, Andrew W. Moore:
A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. 651-658 - Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller:
Link Prediction in Relational Data. 659-666 - Andrew Rabinovich, Sameer Agarwal, Casey Laris, Jeffrey H. Price, Serge J. Belongie:
Unsupervised Color Decomposition Of Histologically Stained Tissue Samples. 667-674 - Su-In Lee, Serafim Batzoglou:
ICA-based Clustering of Genes from Microarray Expression Data. 675-682 - Darya Chudova, Christopher E. Hart, Eric Mjolsness, Padhraic Smyth:
Gene Expression Clustering with Functional Mixture Models. 683-690 - Maneesh Sahani, Srikantan S. Nagarajan:
Reconstructing MEG Sources with Unknown Correlations. 693-700 - Saori C. Tanaka, Kenji Doya, Go Okada, Kazutaka Ueda, Yasumasa Okamoto, Shigeto Yamawaki:
Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales. 701-708 - Xuerui Wang, Rebecca A. Hutchinson, Tom M. Mitchell:
Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects. 709-716 - Roland Vollgraf, Michael Scholz, Ian A. Meinertzhagen, Klaus Obermayer:
Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression. 717-724 - Yu Zhou, Steven G. Mason, Gary E. Birch:
Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface. 725-732 - Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
Increase Information Transfer Rates in BCI by CSP Extension to Multi-class. 733-740 - Sung Chan Jun, Barak A. Pearlmutter:
Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron. 741-748 - Carl Edward Rasmussen, Malte Kuss:
Gaussian Processes in Reinforcement Learning. 751-758 - Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun:
Applying Metric-Trees to Belief-Point POMDPs. 759-766 - Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thrun:
ARA*: Anytime A* with Provable Bounds on Sub-Optimality. 767-774 - Georgios Theocharous, Leslie Pack Kaelbling:
Approximate Planning in POMDPs with Macro-Actions. 775-782 - Natalia Hernandez-Gardiol, Leslie Pack Kaelbling:
Envelope-based Planning in Relational MDPs. 783-790 - David C. Parkes, Satinder Singh:
An MDP-Based Approach to Online Mechanism Design. 791-798 - Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry:
Autonomous Helicopter Flight via Reinforcement Learning. 799-806 - Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling:
All learning is Local: Multi-agent Learning in Global Reward Games. 807-814 - Daniela Pucci de Farias, Nimrod Megiddo:
How to Combine Expert (and Novice) Advice when Actions Impact the Environment? 815-822 - Pascal Poupart, Craig Boutilier:
Bounded Finite State Controllers. 823-830 - J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. 831-838 - Arnab Nilim, Laurent El Ghaoui:
Robustness in Markov Decision Problems with Uncertain Transition Matrices. 839-846 - Alan Fern, Sung Wook Yoon, Robert Givan:
Approximate Policy Iteration with a Policy Language Bias. 847-854 - Matthew R. Rudary, Satinder Singh:
A Nonlinear Predictive State Representation. 855-862 - Xiao Feng Wang, Tuomas Sandholm:
Learning Near-Pareto-Optimal Conventions in Polynomial Time. 863-870 - Gerald Tesauro:
Extending Q-Learning to General Adaptive Multi-Agent Systems. 871-878 - Curt A. Bererton, Geoffrey J. Gordon, Sebastian Thrun:
Auction Mechanism Design for Multi-Robot Coordination. 879-886 - Ciamac Cyrus Moallemi, Benjamin Van Roy:
Distributed Optimization in Adaptive Networks. 887-894 - Milos Hauskrecht, Branislav Kveton:
Linear Program Approximations for Factored Continuous-State Markov Decision Processes. 895-902 - Arnulf B. A. Graf, Felix A. Wichmann:
Insights from Machine Learning Applied to Human Visual Classification. 905-912 - Virginia R. de Sa:
Sensory Modality Segregation. 913-920 - Artur S. d'Avila Garcez, Luís C. Lamb:
Reasoning about Time and Knowledge in Neural Symbolic Learning Systems. 921-928 - Marc Toussaint:
Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System. 926-936 - Woojae Kim, Daniel J. Navarro, Mark A. Pitt, In Jae Myung:
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science. 937-944 - David Philipona, J. Kevin O'Regan, Jean-Pierre Nadal, Olivier J. M. D. Coenen:
Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors. 945-952 - Thomas L. Griffiths, Joshua B. Tenenbaum:
From Algorithmic to Subjective Randomness. 953-960 - Zach Solan, David Horn, Eytan Ruppin, Shimon Edelman:
Unsupervised Context Sensitive Language Acquisition from a Large Corpus. 961-968 - Yuuya Sugita, Jun Tani:
A Holistic Approach to Compositional Semantics: A Connectionist Model and Robot Experiments. 969-976 - Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. Gordon, David S. Touretzky:
Model Uncertainty in Classical Conditioning. 977-984 - Reid R