NIPS 2001: Vancouver, British Columbia, Canada

@proceedings{DBLP:conf/nips/2001,
  editor    = {Thomas G. Dietterich and
               Suzanna Becker and
               Zoubin Ghahramani},
  title     = {Advances in Neural Information Processing Systems 14 [Neural
               Information Processing Systems: Natural and Synthetic, NIPS 2001,
               December 3-8, 2001, Vancouver, British Columbia, Canada]},
  booktitle = {NIPS},
  publisher = {MIT Press},
  year      = {2001},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

• 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, R. 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, J. 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
  
• A. d'Avella, M. C. Tresch:
  Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies. 141-148
  
• J. A. Beintema, A. V. van den Berg, M. 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, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby:
  Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway. 173-180
  
• H. Colonius, A. Diederich:
  A Maximum-Likelihood Approach to Modeling Multisensory Enhancement. 181-187
  
• Peter Dayan, Angela J. Yu:
  ACh, Uncertainty, and Cortical Inference. 189-196
  
• O. 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
  
• N. Matsumoto, M. 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 P. 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
  
• M. 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, Alex 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
  
• M. Leisink, B. 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. M. Wong, F. Li:
  Fast Parameter Estimation Using Green's Functions. 535-542
  
• T. 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
  
• J. Bi, K. 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, S. 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 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, Alexander 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 Jaakkola, Hava T. Siegelmann:
  Active Information Retrieval. 777-784
  
• Jyrki Kivinen, Alex 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 P. Singh:
  An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. 817-823
  
• Peter Meinicke, Helge Ritter:
  Quantizing Density Estimators. 825-832
  
• K. 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 Seeger:
  Covariance Kernels from Bayesian Generative Models. 905-912
  
• Eran Segal, Daphne Koller, Dirk Ormoneit:
  Probabilistic Abstraction Hierarchies. 913-920
  
• Hiroshi Shimodaira, K.-I. 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 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 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
  
• B. 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
  
• T. 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
  
• A. Bofill, A. F. Murray, D. 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 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
  
• W. 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 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
  
• S. Parveen, P. Green:
  Speech Recognition with Missing Data using Recurrent Neural Nets. 1189-1195
  
• N. Smith, Mark J. F. Gales:
  Speech Recognition using SVMs. 1197-1204
  
• K. Yao, S. 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
  
• B. 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 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, G. Andreani, C. 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 B. 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 P. 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 Ritter:
  Hyperbolic Self-Organizing Maps for Semantic Navigation. 1417-1424
  
• John C. Platt, Christopher J. C. Burges, S. Swenson, C. Weare, A. 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 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-Hour 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 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 P. 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