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ORCIDNIPS 2001: Vancouver, British Columbia, Canada
- Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani:
Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]. MIT Press 2001 - Aaron C. Courville, David S. Touretzky:
Modeling Temporal Structure in Classical Conditioning. 3-10 - Peter Dayan:
Motivated Reinforcement Learning. 11-18 - Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Nathan Intrator:
Probabilistic principles in unsupervised learning of visual structure: human data and a model. 19-26 - David Jacobs, Bas Rokers, Archisman Rudra, Zili Liu:
Fragment Completion in Humans and Machines. 27-34 - Dan Klein, Christopher D. Manning:
Natural Language Grammar Induction Using a Constituent-Context Model. 35-42 - Michael Kositsky, Andrew G. Barto:
The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay. 43-50 - Michael C. Mozer, Michael D. Colagrosso, David E. Huber:
A Rational Analysis of Cognitive Control in a Speeded Discrimination Task. 51-57 - S. Narayanan, Daniel Jurafsky:
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing. 59-65 - Michiro Negishi, Stephen Jose Hanson:
Grammar Transfer in a Second Order Recurrent Neural Network. 67-73 - Randall C. O'Reilly, Richard S. Busby:
Generalizable Relational Binding from Coarse-coded Distributed Representations. 75-82 - Randall C. O'Reilly, R. Soto:
A Model of the Phonological Loop: Generalization and Binding. 83-90 - Mark A. Paskin:
Grammatical Bigrams. 91-97 - Bob Rehder:
Causal Categorization with Bayes Nets. 99-105 - Wheeler Ruml:
Constructing Distributed Representations Using Additive Clustering. 107-114 - Jonathan L. Shapiro, John Wearden:
Reinforcement Learning and Time Perception -- a Model of Animal Experiments. 115-122 - Daniel Yarlett, Michael Ramscar:
A Quantitative Model of Counterfactual Reasoning. 123-130 - Giorgio A. Ascoli, Alexei V. Samsonovich:
Bayesian morphometry of hippocampal cells suggests same-cell somatodendritic repulsion. 133-139 - Andrea d'Avella, Matthew C. Tresch:
Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies. 141-148 - Jaap A. Beintema, Albert V. van den Berg, Markus Lappe:
Receptive field structure of flow detectors for heading perception. 149-156 - Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
Classifying Single Trial EEG: Towards Brain Computer Interfacing. 157-164 - Neil Burgess, Tom Hartley:
Orientational and Geometric Determinants of Place and Head-direction. 165-172 - Gal Chechik, Amir Globerson, Michael J. Anderson, Eric D. Young, Israel Nelken, Naftali Tishby:
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway. 173-180 - Hans Colonius, Adele Diederich:
A Maximum-Likelihood Approach to Modeling Multisensory Enhancement. 181-187 - Peter Dayan, Angela J. Yu:
ACh, Uncertainty, and Cortical Inference. 189-196 - Opher Donchin, Reza Shadmehr:
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field. 197-203 - Julian Eggert, Berthold Bäuml:
Exact differential equation population dynamics for integrate-and-fire neurons. 205-212 - Yun Gao, Michael J. Black, Elie Bienenstock, Shy Shoham, John P. Donoghue:
Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex. 213-220 - Richard H. R. Hahnloser, Xiaohui Xie, H. Sebastian Seung:
A theory of neural integration in the head-direction system. 221-228 - Ádám Kepecs, S. Raghavachari:
3 state neurons for contextual processing. 229-236 - Peter E. Latham:
Associative memory in realistic neuronal networks. 237-244 - Narihisa Matsumoto, Masato Okada:
Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity. 245-252 - Hiroyuki Nakahara, Shun-ichi Amari:
Information-Geometric Decomposition in Spike Analysis. 253-260 - Antonino Casile, Michele Rucci:
Eye movements and the maturation of cortical orientation selectivity. 261-267 - Odelia Schwartz, E. J. Chichilnisky, Eero P. Simoncelli:
Characterizing Neural Gain Control using Spike-triggered Covariance. 269-276 - Maoz Shamir, Haim Sompolinsky:
Correlation Codes in Neuronal Populations. 277-284 - Jesper Tegnér, Ádám Kepecs:
Why Neuronal Dynamics Should Control Synaptic Learning Rules. 285-292 - Thomas Trappenberg, Edmund T. Rolls, Simon M. Stringer:
Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes. 293-300 - Gregor Wenning, Klaus Obermayer:
Activity Driven Adaptive Stochastic Resonance. 301-308 - B. D. Wright, Kamal Sen, William Bialek, A. J. Doupe:
Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds. 309-316 - Si Wu, Shun-ichi Amari:
Neural Implementation of Bayesian Inference in Population Codes. 317-323 - Xiaohui Xie, Martin A. Giese:
Generating velocity tuning by asymmetric recurrent connections. 325-332 - Dimitris Achlioptas, Frank McSherry, Bernhard Schölkopf:
Sampling Techniques for Kernel Methods. 335-342 - Shun-ichi Amari, Hyeyoung Park, Tomoko Ozeki:
Geometrical Singularities in the Neuromanifold of Multilayer Perceptrons. 343-350 - Mikio L. Braun, Joachim M. Buhmann:
The Noisy Euclidean Traveling Salesman Problem and Learning. 351-358 - Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
On the Generalization Ability of On-Line Learning Algorithms. 359-366 - Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola:
On Kernel-Target Alignment. 367-373 - Sanjoy Dasgupta, Michael L. Littman, David A. McAllester:
PAC Generalization Bounds for Co-training. 375-382 - Anita C. Faul, Michael E. Tipping:
Analysis of Sparse Bayesian Learning. 383-389 - Ralf Herbrich, Robert C. Williamson:
Algorithmic Luckiness. 391-397 - Marcus Hutter:
Distribution of Mutual Information. 399-406 - Shiro Ikeda, Toshiyuki Tanaka, Shun-ichi Amari:
Information Geometrical Framework for Analyzing Belief Propagation Decoder. 407-414 - Hilbert J. Kappen, Wim Wiegerinck:
Novel iteration schemes for the Cluster Variation Method. 415-422 - Roni Khardon, Dan Roth, Rocco A. Servedio:
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms. 423-430 - Jon M. Kleinberg:
Small-World Phenomena and the Dynamics of Information. 431-438 - Adam Kowalczyk, Alexander J. Smola, Robert C. Williamson:
Kernel Machines and Boolean Functions. 439-446 - Guy Lebanon, John D. Lafferty:
Boosting and Maximum Likelihood for Exponential Models. 447-454 - Martijn A. R. Leisink, Bert Kappen:
Means, Correlations and Bounds. 455-462 - Dörthe Malzahn, Manfred Opper:
A Variational Approach to Learning Curves. 463-469 - Ilya Nemenman, F. Shafee, William Bialek:
Entropy and Inference, Revisited. 471-478 - Manfred Opper, Robert Urbanczik:
Asymptotic Universality for Learning Curves of Support Vector Machines. 479-486 - Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth:
On the Convergence of Leveraging. 487-494 - Magnus Rattray, Gleb Basalyga:
Scaling Laws and Local Minima in Hebbian ICA. 495-501 - M. Schmitt:
Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks. 503-510 - John Shawe-Taylor, Nello Cristianini, Jaz S. Kandola:
On the Concentration of Spectral Properties. 511-517 - Peter Sollich:
Gaussian Process Regression with Mismatched Models. 519-526 - Toshiyuki Tanaka, Shiro Ikeda, Shun-ichi Amari:
Information-Geometrical Significance of Sparsity in Gallager Codes. 527-534 - K. Y. Michael Wong, F. Li:
Fast Parameter Estimation Using Green's Functions. 535-542 - Tong Zhang:
Generalization Performance of Some Learning Problems in Hilbert Functional Spaces. 543-550 - Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise:
Semi-supervised MarginBoost. 553-560 - Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Rao-Blackwellised Particle Filtering via Data Augmentation. 561-567 - Francis R. Bach, Michael I. Jordan:
Thin Junction Trees. 569-576 - Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen:
The Infinite Hidden Markov Model. 577-584 - Mikhail Belkin, Partha Niyogi:
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. 585-591 - Jinbo Bi, Kristin P. Bennett:
Duality, Geometry, and Support Vector Regression. 593-600 - David M. Blei, Andrew Y. Ng, Michael I. Jordan:
Latent Dirichlet Allocation. 601-608 - Olivier Chapelle, Bernhard Schölkopf:
Incorporating Invariances in Non-Linear Support Vector Machines. 609-616 - Michael Collins, Sanjoy Dasgupta, Robert E. Schapire:
A Generalization of Principal Components Analysis to the Exponential Family. 617-624 - Michael Collins, Nigel Duffy:
Convolution Kernels for Natural Language. 625-632 - Ronan Collobert, Samy Bengio, Yoshua Bengio:
A Parallel Mixture of SVMs for Very Large Scale Problems. 633-640 - Koby Crammer, Yoram Singer:
Pranking with Ranking. 641-647 - Nello Cristianini, John Shawe-Taylor, Jaz S. Kandola:
Spectral Kernel Methods for Clustering. 649-655 - Lehel Csató, Manfred Opper, Ole Winther:
TAP Gibbs Free Energy, Belief Propagation and Sparsity. 657-663 - Carlotta Domeniconi, Dimitrios Gunopulos:
Adaptive Nearest Neighbor Classification Using Support Vector Machines. 665-672 - Pedro M. Domingos, Geoff Hulten:
Learning from Infinite Data in Finite Time. 673-680 - André Elisseeff, Jason Weston:
A kernel method for multi-labelled classification. 681-687 - Daniela Pucci de Farias, Benjamin Van Roy:
Approximate Dynamic Programming via Linear Programming. 689-695 - Mário A. T. Figueiredo:
Adaptive Sparseness Using Jeffreys Prior. 697-704 - Shai Fine, Katya Scheinberg
:
Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM. 705-711 - Dieter Fox:
KLD-Sampling: Adaptive Particle Filters. 713-720 - Brendan J. Frey, Nebojsa Jojic:
Fast, Large-Scale Transformation-Invariant Clustering. 721-727 - Brendan J. Frey, Anitha Kannan, Nebojsa Jojic:
Product Analysis: Learning to Model Observations as Products of Hidden Variables. 729-735 - Brendan J. Frey, Ralf Koetter, Nemanja Petrovic:
Very loopy belief propagation for unwrapping phase images. 737-743 - Polina Golland:
Discriminative Direction for Kernel Classifiers. 745-752 - Patrick Haffner:
Escaping the Convex Hull with Extrapolated Vector Machines. 753-760 - Stefan Harmeling, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller:
Kernel Feature Spaces and Nonlinear Blind Souce Separation. 761-768 - David Horn, Assaf Gottlieb:
The Method of Quantum Clustering. 769-776 - Tommi S. Jaakkola, Hava T. Siegelmann:
Active Information Retrieval. 777-784 - Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Online Learning with Kernels. 785-792 - Jens Kohlmorgen, Steven Lemm:
A Dynamic HMM for On-line Segmentation of Sequential Data. 793-800 - Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan:
Minimax Probability Machine. 801-807 - John Langford, Rich Caruana:
(Not) Bounding the True Error. 809-816 - Michael L. Littman, Michael J. Kearns, Satinder Singh:
An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. 817-823 - Peter Meinicke, Helge J. Ritter:
Quantizing Density Estimators. 825-832 - Kevin P. Murphy, Mark A. Paskin:
Linear-time inference in Hierarchical HMMs. 833-840 - Andrew Y. Ng, Michael I. Jordan:
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. 841-848 - Andrew Y. Ng, Michael I. Jordan, Yair Weiss:
On Spectral Clustering: Analysis and an algorithm. 849-856 - Alberto Paccanaro, Geoffrey E. Hinton:
Learning Hierarchical Structures with Linear Relational Embedding. 857-864 - Marcello Pelillo:
Matching Free Trees with Replicator Equations. 865-872 - Anand Rangarajan, Alan L. Yuille:
MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation. 873-880 - Carl Edward Rasmussen, Zoubin Ghahramani:
Infinite Mixtures of Gaussian Process Experts. 881-888 - Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton:
Global Coordination of Local Linear Models. 889-896 - Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Classification by Mixture Models. 897-904 - Matthias W. Seeger:
Covariance Kernels from Bayesian Generative Models. 905-912 - Eran Segal, Daphne Koller, Dirk Ormoneit:
Probabilistic Abstraction Hierarchies. 913-920 - Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai, Shigeki Sagayama:
Dynamic Time-Alignment Kernel in Support Vector Machine. 921-928 - Noam Slonim, Nir Friedman, Naftali Tishby:
Agglomerative Multivariate Information Bottleneck. 929-936 - Peter Sykacek, Stephen J. Roberts:
Bayesian time series classification. 937-944 - Martin Szummer, Tommi S. Jaakkola:
Partially labeled classification with Markov random walks. 945-952 - Yee Whye Teh, Max Welling:
The Unified Propagation and Scaling Algorithm. 953-960 - Sebastian Thrun, John Langford, Vandi Verma:
Risk Sensitive Particle Filters. 961-968 - Kari Torkkola:
Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions. 969-976 - Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller:
A New Discriminative Kernel From Probabilistic Models. 977-984 - Pascal Vincent, Yoshua Bengio:
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms. 985-992 - Roland Vollgraf, Klaus Obermayer:
Multi Dimensional ICA to Separate Correlated Sources. 993-1000 - Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Tree-based reparameterization for approximate inference on loopy graphs. 1001-1008 - Heiko Wersing:
Learning Lateral Interactions for Feature Binding and Sensory Segmentation. 1009-1016 - Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof:
Products of Gaussians. 1017-1024 - Ran El-Yaniv, Oren Souroujon:
Iterative Double Clustering for Unsupervised and Semi-Supervised Learning. 1025-1032 - Alan L. Yuille, Anand Rangarajan:
The Concave-Convex Procedure (CCCP). 1033-1040 - Bianca Zadrozny:
Reducing multiclass to binary by coupling probability estimates. 1041-1048 - Michael Zibulevsky, Pavel Kisilev, Yehoshua Y. Zeevi, Barak A. Pearlmutter:
Blind Source Separation via Multinode Sparse Representation. 1049-1056 - Hongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming Gu, Horst D. Simon:
Spectral Relaxation for K-means Clustering. 1057-1064 - Tong Zhang:
A General Greedy Approximation Algorithm with Applications. 1065-1072 - Qi Zhang, Sally A. Goldman:
EM-DD: An Improved Multiple-Instance Learning Technique. 1073-1080 - Ji Zhu, Trevor Hastie:
Kernel Logistic Regression and the Import Vector Machine. 1081-1088 - Adria Bofill, Alan F. Murray, Damon P. Thompson:
Citcuits for VLSI Implementation of Temporally Asymmetric Hebbian Learning. 1091-1098 - Roman Genov, Gert Cauwenberghs:
Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines. 1099-1105 - Shih-Chii Liu, Jörg Kramer, Giacomo Indiveri, Tobi Delbrück, Rodney J. Douglas:
Orientation-Selective aVLSI Spiking Neurons. 1107-1114 - Takashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata:
An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures. 1115-1122 - Aaron P. Shon, David Hsu, Chris Diorio:
Learning Spike-Based Correlations and Conditional Probabilities in Silicon. 1123-1130 - Toshihiko Yamasaki, Tadashi Shibata:
Analog Soft-Pattern-Matching Classifier using Floating-Gate MOS Technology. 1131-1138 - Jeff A. Bilmes, Gang Ji, Marina Meila:
Intransitive Likelihood-Ratio Classifiers. 1141-1148 - Andrew D. Brown, Geoffrey E. Hinton:
Relative Density Nets: A New Way to Combine Backpropagation with HMM's. 1149-1156 - William M. Campbell:
A Sequence Kernel and its Application to Speaker Recognition. 1157-1163 - Brendan J. Frey, Trausti T. Kristjansson, Li Deng, Alex Acero:
ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition. 1165-1171 - John R. Hershey, Michael Casey:
Audio-Visual Sound Separation Via Hidden Markov Models. 1173-1180 - Frank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller:
Estimating the Reliability of ICA Projections. 1181-1188 - Shahla Parveen, Phil D. Green:
Speech Recognition with Missing Data using Recurrent Neural Nets. 1189-1195 - N. Smith, Mark J. F. Gales:
Speech Recognition using SVMs. 1197-1204 - Kaisheng Yao, Satoshi Nakamura:
Sequential Noise Compensation by Sequential Monte Carlo Method. 1205-1212 - Stuart N. Wrigley, Guy J. Brown:
A Neural Oscillator Model of Auditory Selective Attention. 1213-1220 - Benjamin T. Backus:
Perceptual Metamers in Stereoscopic Vision. 1223-1230 - James M. Coughlan, Alan L. Yuille:
The g Factor: Relating Distributions on Features to Distributions on Images. 1231-1238 - Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso A. Poggio:
Categorization by Learning and Combining Object Parts. 1239-1245 - Laurent Itti, Jochen Braun, Christof Koch:
Modeling the Modulatory Effect of Attention on Human Spatial Vision. 1247-1254 - Marzia Polito, Pietro Perona:
Grouping and dimensionality reduction by locally linear embedding. 1255-1262 - Rómer Rosales, Stan Sclaroff:
Learning Body Pose via Specialized Maps. 1263-1270 - Silvio P. Sabatini, Fabio Solari, Giulia Andreani, Chiara Bartolozzi, Giacomo M. Bisio:
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth. 1271-1278 - Javid Sadr, Sayan Mukherjee, K. Thoresz, Pawan Sinha:
The Fidelity of Local Ordinal Encoding. 1279-1286 - Yang Song, Luis Goncalves, Pietro Perona:
Unsupervised Learning of Human Motion Models. 1287-1294 - Chris Stauffer, Erik G. Miller, Kinh Tieu:
Transform-invariant Image Decomposition with Similarity Templates. 1295-1302 - Antonio Torralba:
Contextual Modulation of Target Saliency. 1303-1310 - Paul A. Viola, Michael J. Jones:
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade. 1311-1318 - Lance R. Williams, John W. Zweck:
A Rotation and Translation Invariant Discrete Saliency Network. 1319-1326 - Stella X. Yu, Jianbo Shi:
Grouping with Bias. 1327-1334 - Timothy X. Brown:
Switch Packet Arbitration via Queue-Learning. 1337-1344 - Igor V. Cadez, Paul S. Bradley:
Model Based Population Tracking and Automatic Detection of Distribution Changes. 1345-1352 - Igor V. Cadez, Padhraic Smyth:
Bayesian Predictive Profiles With Applications to Retail Transaction Data. 1353-1360 - Ali Taylan Cemgil, Bert Kappen:
Tempo tracking and rhythm quantization by sequential Monte Carlo. 1361-1368 - Nicolas Chapados, Yoshua Bengio, Pascal Vincent, Joumana Ghosn, Charles Dugas, Ichiro Takeuchi, Linyan Meng:
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference. 1369-1376 - Judy A. Franklin:
Improvisation and Learning. 1377-1384 - Thomas L. Griffiths, Joshua B. Tenenbaum:
Using Vocabulary Knowledge in Bayesian Multinomial Estimation. 1385-1392 - Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone:
Cobot: A Social Reinforcement Learning Agent. 1393-1400 - Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor:
Optimising Synchronisation Times for Mobile Devices. 1401-1408 - Michael C. Mozer, Robert H. Dodier, Michael D. Colagrosso, Cesar Guerra-Salcedo, Richard H. Wolniewicz:
Prodding the ROC Curve: Constrained Optimization of Classifier Performance. 1409-1415 - Jörg Ontrup, Helge J. Ritter:
Hyperbolic Self-Organizing Maps for Semantic Navigation. 1417-1424 - John C. Platt, Christopher J. C. Burges, Steven Swenson, Christopher Weare, Alice Zheng:
Learning a Gaussian Process Prior for Automatically Generating Music Playlists. 1425-1432 - Christopher Raphael:
A Bayesian Network for Real-Time Musical Accompaniment. 1433-1439 - Matthew Richardson, Pedro M. Domingos:
The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank. 1441-1448 - Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen:
Active Learning in the Drug Discovery Process. 1449-1456 - Ming-Hsuan Yang:
Face Recognition Using Kernel Methods. 1457-1464 - Hans-Georg Zimmermann, Ralph Neuneier, Ralph Grothmann:
Active Portfolio-Management based on Error Correction Neural Networks. 1465-1472 - Bram Bakker:
Reinforcement Learning with Long Short-Term Memory. 1475-1482 - Yu-Han Chang, Leslie Pack Kaelbling:
Playing is believing: The role of beliefs in multi-agent learning. 1483-1490 - Thomas G. Dietterich, Xin Wang:
Batch Value Function Approximation via Support Vectors. 1491-1498 - Eyal Even-Dar, Yishay Mansour:
Convergence of Optimistic and Incremental Q-Learning. 1499-1506 - Evan Greensmith, Peter L. Bartlett, Jonathan Baxter:
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. 1507-1514 - Gregory Z. Grudic, Lyle H. Ungar:
Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning. 1515-1522 - Carlos Guestrin, Daphne Koller, Ronald Parr:
Multiagent Planning with Factored MDPs. 1523-1530 - Sham M. Kakade:
A Natural Policy Gradient. 1531-1538 - Sven Koenig, Maxim Likhachev:
Incremental A*. 1539-1546 - Michail G. Lagoudakis, Ronald Parr:
Model-Free Least-Squares Policy Iteration. 1547-1554 - Michael L. Littman, Richard S. Sutton, Satinder Singh:
Predictive Representations of State. 1555-1561 - Shie Mannor, Nahum Shimkin:
The Steering Approach for Multi-Criteria Reinforcement Learning. 1563-1570 - Rémi Munos:
Efficient Resources Allocation for Markov Decision Processes. 1571-1578 - Dale Schuurmans, Relu Patrascu:
Direct value-approximation for factored MDPs. 1579-1586 - Xin Wang, Thomas G. Dietterich:
Stabilizing Value Function Approximation with the BFBP Algorithm. 1587-1594
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