NIPS 2002: Vancouver, British Columbia, Canada

• Dan Klein, Christopher D. Manning:
  Fast Exact Inference with a Factored Model for Natural Language Parsing. 3-10
  
• Thomas L. Griffiths, Mark Steyvers:
  Prediction and Semantic Association. 11-18
  
• Szabolcs Káli, Peter Dayan:
  Replay, Repair and Consolidation. 19-26
  
• Emanuel Todorov, Michael I. Jordan:
  A Minimal Intervention Principle for Coordinated Movement. 27-34
  
• David Fass, Jacob Feldman:
  Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories. 35-34
  
• Joshua B. Tenenbaum, Thomas L. Griffiths:
  Theory-Based Causal Inference. 35-42
  
• Willem H. Zuidema:
  How the Poverty of the Stimulus Solves the Poverty of the Stimulus. 43-50
  
• Neville E. Sanjana, Joshua B. Tenenbaum:
  Bayesian Models of Inductive Generalization. 51-58
  
• Daniel J. Navarro, Michael D. Lee:
  Combining Dimensions and Features in Similarity-Based Representations. 59-66
  
• Kenneth J. Malmberg, René Zeelenberg, Richard M. Shiffrin:
  Modeling Midazolam's Effect on the Hippocampus and Recognition Memory. 67-66
  
• David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum:
  Dynamical Causal Learning. 67-74
  
• Robert A. Jacobs, Melissa Dominguez:
  Visual Development Aids the Acquisition of Motion Velocity Sensitivities. 75-82
  
• Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky:
  Timing and Partial Observability in the Dopamine System. 83-90
  
• Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman:
  Automatic Acquisition and Efficient Representation of Syntactic Structures. 91-98
  
• Michael DeWeese, Anthony M. Zador:
  Binary Coding in Auditory Cortex. 101-108
  
• Maneesh Sahani, Jennifer F. Linden:
  How Linear are Auditory Cortical Responses?. 109-116
  
• Wei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, Mijail Serruya, A. Shaikhouni, John P. Donoghue:
  Neural Decoding of Cursor Motion Using a Kalman Filter. 117-124
  
• Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia:
  Spikernels: Embedding Spiking Neurons in Inner-Product Spaces. 125-132
  
• Christian K. Machens, Michael Wehr, Anthony M. Zador:
  Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory Cortex. 133-140
  
• Jarmo Hurri, Aapo Hyvärinen:
  Temporal Coherence, Natural Image Sequences, and the Visual Cortex. 141-148
  
• David Barber:
  Learning in Spiking Neural Assemblies. 149-156
  
• Angela J. Yu, Peter Dayan:
  Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. 157-164
  
• Aaron J. Gruber, Sara A. Solla, James C. Houk:
  Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons. 165-172
  
• Liam Paninski:
  Convergence Properties of Some Spike-Triggered Analysis Techniques. 173-180
  
• Dmitri B. Chklovskii, Armen Stepanyants:
  Branching Law for Axons. 181-188
  
• Matthias Bethge, David Rotermund, Klaus Pawelzik:
  Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution. 189-196
  
• Elad Schneidman, William Bialek, Michael J. Berry II:
  An Information Theoretic Approach to the Functional Classification of Neurons. 197-204
  
• Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, Terrence J. Sejnowski:
  Factorial Coding of Color in Primary Visual Cortex. 205-212
  
• Wolfgang Maass, Thomas Natschläger, Henry Markram:
  A Model for Real-Time Computation in Generic Neural Microcircuits. 213-220
  
• Peter Dayan, Maneesh Sahani, Gregoire Deback:
  Adaptation and Unsupervised Learning. 221-228
  
• Alex Holub, Gilles Laurent, Pietro Perona:
  A Digital Antennal Lobe for Pattern Equalization: Analysis and Design. 229-236
  
• Michael Eisele, Kenneth D. Miller:
  Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule. 237-244
  
• Luk-Chong Yeung, Brian S. Blais, Leon N. Cooper, Harel Z. Shouval:
  Selectivity and Metaplasticity in a Unified Calcium-Dependent Model. 245-252
  
• Alistair Bray, Dominique Martinez:
  Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images. 253-260
  
• Tatyana Sharpee, Nicole C. Rust, William Bialek:
  Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals. 261-268
  
• Arunava Banerjee, Alexandre Pouget:
  Dynamical Constraints on Computing with Spike Timing in the Cortex. 269-276
  
• Patrik O. Hoyer, Aapo Hyvärinen:
  Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. 277-284
  
• Alon Fishbach, Bradford J. May:
  A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise. 285-292
  
• Christian W. Eurich:
  An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli. 293-300
  
• Maneesh Sahani, Jennifer F. Linden:
  Evidence Optimization Techniques for Estimating Stimulus-Response Functions. 301-308
  
• Duane Q. Nykamp:
  Reconstructing Stimulus-Driven Neural Networks from Spike Times. 309-316
  
• Ron Meir, Tong Zhang:
  Data-Dependent Bounds for Bayesian Mixture Methods. 319-326
  
• Dörthe Malzahn, Manfred Opper:
  A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. 327-334
  
• Noam Slonim, Yair Weiss:
  Maximum Likelihood and the Information Bottleneck. 335-342
  
• Tom Heskes:
  Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. 343-350
  
• David A. McAllester, Luis E. Ortiz:
  Concentration Inequalities for the Missing Mass and for Histogram Rule Error. 351-358
  
• Clayton Scott, Robert Nowak:
  Dyadic Classification Trees via Structural Risk Minimization. 359-366
  
• John Shawe-Taylor, Christopher K. I. Williams:
  The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. 367-374
  
• John D. Lafferty, Guy Lebanon:
  Information Diffusion Kernels. 375-382
  
• Jonathan L. Shapiro:
  Scaling of Probability-Based Optimization Algorithms. 383-390
  
• Sumio Watanabe, Shun-ichi Amari:
  The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities. 391-398
  
• Olivier Bousquet, Daniel J. L. Herrmann:
  On the Complexity of Learning the Kernel Matrix. 399-406
  
• Tatsuto Murayama, Masato Okada:
  Rate Distortion Function in the Spin Glass State: A Toy Model. 407-414
  
• Guy Lebanon, John D. Lafferty:
  Conditional Models on the Ranking Poset. 415-422
  
• John Langford, John Shawe-Taylor:
  PAC-Bayes & Margins. 423-430
  
• Eric Allender, Sanjeev Arora, Michael Kearns, Cristopher Moore, Alexander Russell:
  A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. 431-437
  
• Wim Wiegerinck, Tom Heskes:
  Fractional Belief Propagation. 438-445
  
• Jon M. Kleinberg:
  An Impossibility Theorem for Clustering. 446-453
  
• Tong Zhang:
  Effective Dimension and Generalization of Kernel Learning. 454-461
  
• Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
  Margin Analysis of the LVQ Algorithm. 462-469
  
• Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
  Margin-Based Algorithms for Information Filtering. 470-477
  
• Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
  Hyperkernels. 478-485
  
• Carl Edward Rasmussen, Zoubin Ghahramani:
  Bayesian Monte Carlo. 489-496
  
• Bin Wu, K. Y. Michael Wong, David Bodoff:
  Mean Field Approach to a Probabilistic Model in Information Retrieval. 497-504
  
• Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell:
  Distance Metric Learning with Application to Clustering with Side-Information. 505-512
  
• Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
  Adapting Codes and Embeddings for Polychotomies. 513-520
  
• Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
  Knowledge-Based Support Vector Machine Classifiers. 521-528
  
• Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith:
  Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting. 529-536
  
• Koby Crammer, Joseph Keshet, Yoram Singer:
  Kernel Design Using Boosting. 537-544
  
• Sepp Hochreiter, Michael Mozer, Klaus Obermayer:
  Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. 545-552
  
• Yves Grandvalet, Stéphane Canu:
  Adaptive Scaling for Feature Selection in SVMs. 553-560
  
• Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann:
  Support Vector Machines for Multiple-Instance Learning. 561-568
  
• S. V. N. Vishwanathan, Alexander J. Smola:
  Fast Kernels for String and Tree Matching. 569-576
  
• Geoffrey J. Gordon:
  Generalized² Linear² Models. 577-584
  
• Olivier Chapelle, Jason Weston, Bernhard Schölkopf:
  Cluster Kernels for Semi-Supervised Learning. 585-592
  
• Herbert Jaeger:
  Adaptive Nonlinear System Identification with Echo State Networks. 593-600
  
• Corinna Cortes, Patrick Haffner, Mehryar Mohri:
  Rational Kernels. 601-608
  
• Neil D. Lawrence, Matthias Seeger, Ralf Herbrich:
  Fast Sparse Gaussian Process Methods: The Informative Vector Machine. 609-616
  
• Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann:
  Stability-Based Model Selection. 617-624
  
• Martin H. C. Law, Anil K. Jain, Mário A. T. Figueiredo:
  Feature Selection in Mixture-Based Clustering. 625-632
  
• Craig Saunders, John Shawe-Taylor, Alexei Vinokourov:
  String Kernels, Fisher Kernels and Finite State Automata. 633-640
  
• Saharon Rosset, Eran Segal:
  Boosting Density Estimation. 641-648
  
• Trevor Hastie, Robert Tibshirani:
  Independent Components Analysis through Product Density Estimation. 649-656
  
• Jaz S. Kandola, John Shawe-Taylor, Nello Cristianini:
  Learning Semantic Similarity. 657-664
  
• Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
  Self Supervised Boosting. 665-672
  
• Alexander G. Gray, Bernd Fischer, Johann Schumann, Wray L. Buntine:
  Automatic Derivation of Statistical Algorithms: The EM Family and Beyond. 673-680
  
• Balázs Kégl:
  Intrinsic Dimension Estimation Using Packing Numbers. 681-688
  
• Chakra Chennubhotla, Allan D. Jepson:
  Half-Lives of EigenFlows for Spectral Clustering. 689-696
  
• Harald Steck, Tommi Jaakkola:
  On the Dirichlet Prior and Bayesian Regularization. 697-704
  
• Vin de Silva, Joshua B. Tenenbaum:
  Global Versus Local Methods in Nonlinear Dimensionality Reduction. 705-712
  
• David Barber:
  Dynamic Bayesian Networks with Deterministic Latent Tables. 713-720
  
• Naonori Ueda, Kazumi Saito:
  Parametric Mixture Models for Multi-Labeled Text. 721-728
  
• Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller:
  Clustering with the Fisher Score. 729-736
  
• Peter Sykacek, Stephen J. Roberts:
  Adaptive Classification by Variational Kalman Filtering. 737-744
  
• Baback Moghaddam, Gregory Shakhnarovich:
  Boosted Dyadic Kernel Discriminants. 745-752
  
• Finnegan Southey, Dale Schuurmans, Ali Ghodsi:
  Regularized Greedy Importance Sampling. 753-760
  
• Elzbieta Pekalska, David M. J. Tax, Robert P. W. Duin:
  One-Class LP Classifiers for Dissimilarity Representations. 761-768
  
• Thomas Strohmann, Gregory Z. Grudic:
  A Formulation for Minimax Probability Machine Regression. 769-776
  
• Christopher M. Bishop, David J. Spiegelhalter, John M. Winn:
  VIBES: A Variational Inference Engine for Bayesian Networks. 777-784
  
• James D. Park, Adnan Darwiche:
  A Differential Semantics for Jointree Algorithms. 785-784
  
• Sariel Har-Peled, Dan Roth, Dav Zimak:
  Constraint Classification for Multiclass Classification and Ranking. 785-792
  
• Luis E. Ortiz, Michael J. Kearns:
  Nash Propagation for Loopy Graphical Games. 793-800
  
• Dan Pelleg, Andrew W. Moore:
  Using Tarjan's Red Rule for Fast Dependency Tree Construction. 801-808
  
• Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky:
  Exact MAP Estimates by (Hyper)tree Agreement. 809-816
  
• Volker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller:
  Going Metric: Denoising Pairwise Data. 817-824
  
• Pascal Vincent, Yoshua Bengio:
  Manifold Parzen Windows. 825-832
  
• Geoffrey E. Hinton, Sam T. Roweis:
  Stochastic Neighbor Embedding. 833-840
  
• Yee Whye Teh, Sam T. Roweis:
  Automatic Alignment of Local Representations. 841-848
  
• David Cohn:
  Informed Projections. 849-856
  
• Gal Chechik, Naftali Tishby:
  Extracting Relevant Structures with Side Information. 857-864
  
• Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari:
  Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting. 865-872
  
• Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik:
  Kernel Dependency Estimation. 873-880
  
• Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski:
  Handling Missing Data with Variational Bayesian Learning of ICA. 881-888
  
• Sepp Hochreiter, Klaus Obermayer:
  Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers. 889-896
  
• Rong Jin, Zoubin Ghahramani:
  Learning with Multiple Labels. 897-904
  
• Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan:
  Robust Novelty Detection with Single-Class MPM. 905-912
  
• Nicholas P. Hughes, David Lowe:
  Artefactual Structure from Least-Squares Multidimensional Scaling. 913-920
  
• Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor:
  The Decision List Machine. 921-928
  
• Mikhail Belkin, Partha Niyogi:
  Using Manifold Stucture for Partially Labeled Classification. 929-936
  
• Amnon Shashua, Anat Levin:
  Ranking with Large Margin Principle: Two Approaches. 937-944
  
• Ofer Dekel, Yoram Singer:
  Multiclass Learning by Probabilistic Embeddings. 945-952
  
• Anton Schwaighofer, Volker Tresp:
  Transductive and Inductive Methods for Approximate Gaussian Process Regression. 953-960
  
• Matthew Brand:
  Charting a Manifold. 961-968
  
• Albert E. Parker, Tomás Gedeon, Alexander Dimitrov:
  Annealing and the Rate Distortion Problem. 969-976
  
• Yasemin Altun, Thomas Hofmann, Mark Johnson:
  Discriminative Learning for Label Sequences via Boosting. 977-984
  
• Peter Meinicke, Thorsten Twellmann, Helge Ritter:
  Discriminative Densities from Maximum Contrast Estimation. 985-992
  
• Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang:
  FloatBoost Learning for Classification. 993-1000
  
• Joaquin Quiñonero Candela, Ole Winther:
  Incremental Gaussian Processes. 1001-1008
  
• Francis R. Bach, Michael I. Jordan:
  Learning Graphical Models with Mercer Kernels. 1009-1016
  
• David A. Ross, Richard S. Zemel:
  Multiple Cause Vector Quantization. 1017-1024
  
• Martin Szummer, Tommi Jaakkola:
  Information Regularization with Partially Labeled Data. 1025-1032
  
• E. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen:
  Derivative Observations in Gaussian Process Models of Dynamic Systems. 1033-1040
  
• Fei Sha, Lawrence K. Saul, Daniel D. Lee:
  Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. 1041-1048
  
• Ali Rahimi, Trevor Darrell:
  Location Estimation with a Differential Update Network. 1049-1056
  
• Cody C. T. Kwok, Dieter Fox, Marina Meila:
  Real-Time Particle Filters. 1057-1064
  
• Alexandre R. S. Romariz, Kelvin Wagner:
  Optoelectronic Implementation of a FitzHugh-Nagumo Neural Model. 1067-1074
  
• Shih-Chii Liu, Malte Boegershausen, Pascal Suter:
  Circuit Model of Short-Term Synaptic Dynamics. 1075-1082
  
• David Hsu, Seth Bridges, Miguel Figueroa, Chris Diorio:
  Adaptive Quantization and Density Estimation in Silicon. 1083-1090
  
• Giacomo Indiveri:
  Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent Plasticity. 1091-1098
  
• Ricardo Carmona-Galán, Francisco Jiménez-Garrido, Rafael Domínguez-Castro, Servando Espejo-Meana, Ángel Rodríguez-Vázquez:
  Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip. 1099-1106
  
• Peter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann, Helge Ritter:
  Improving Transfer Rates in Brain Computer Interfacing: A Case Study. 1107-1114
  
• Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
  Combining Features for BCI. 1115-1122
  
• Jakob Heinzle, Alan Stocker:
  Classifying Patterns of Visual Motion - a Neuromorphic Approach. 1123-1130
  
• Terry Elliott, Jörg Kramer:
  Developing Topography and Ocular Dominance Using Two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity. 1131-1138
  
• Brian Taba, Kwabena Boahen:
  Topographic Map Formation by Silicon Growth Cones. 1139-1146
  
• R. Jacob Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, Gert Cauwenberghs:
  Spike Timing-Dependent Plasticity in the Address Domain. 1147-1154
  
• Seth Bridges, Miguel Figueroa, David Hsu, Chris Diorio:
  Field-Programmable Learning Arrays. 1155-1162
  
• Shantanu Chakrabartty, Gert Cauwenberghs:
  Forward-Decoding Kernel-Based Phone Recognition. 1165-1172
  
• Gil-Jin Jang, Te-Won Lee:
  A Probabilistic Approach to Single Channel Blind Signal Separation. 1173-1180
  
• Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann LeCun:
  Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. 1181-1188
  
• Sachin S. Kajarekar, Hynek Hermansky:
  Analysis of Information in Speech Based on MANOVA. 1189-1196
  
• Patrick J. Wolfe, Simon J. Godsill:
  Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement. 1197-1204
  
• Hagai Attias:
  Source Separation with a Sensor Array Using Graphical Models and Subband Filtering. 1205-1212
  
• Samy Bengio:
  An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition. 1213-1220
  
• Guoning Hu, DeLiang L. Wang:
  Monaural Speech Separation. 1221-1228
  
• Ehud Ben-Reuven, Yoram Singer:
  Discriminative Binaural Sound Localization. 1229-1236
  
• Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
  Application of Variational Bayesian Approach to Speech Recognition. 1237-1244
  
• Anat Levin, Assaf Zomet, Yair Weiss:
  Learning to Perceive Transparency from the Statistics of Natural Scenes. 1247-1254
  
• David R. Martin, Charless Fowlkes, Jitendra Malik:
  Learning to Detect Natural Image Boundaries Using Brightness and Texture. 1255-1262
  
• Anitha Kannan, Nebojsa Jojic, Brendan J. Frey:
  Fast Transformation-Invariant Factor Analysis. 1263-1270
  
• Marian Stewart Bartlett, Gwen Littlewort, Bjorn Braathen, Terrence J. Sejnowski, Javier R. Movellan:
  A Prototype for Automatic Recognition of Spontaneous Facial Actions. 1271-1278
  
• Michael E. Tipping, Christopher M. Bishop:
  Bayesian Image Super-Resolution. 1279-1286
  
• David B. Grimes, Rajesh P. N. Rao:
  A Bilinear Model for Sparse Coding. 1287-1294
  
• Amos J. Storkey:
  Dynamic Structure Super-Resolution. 1295-1302
  
• Kinh Tieu, Erik G. Miller:
  Unsupervised Color Constancy. 1303-1310
  
• Leonid Taycher, John W. Fisher III, Trevor Darrell:
  Recovering Articulated Model Topology from Observed Rigid Motion. 1311-1318
  
• Matthias O. Franz, Javaan S. Chahl:
  Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - a Real-World Test of a Neural Model. 1319-1326
  
• Phil Sallee, Bruno A. Olshausen:
  Learning Sparse Multiscale Image Representations. 1327-1334
  
• William T. Freeman, Antonio Torralba:
  Shape Recipes: Scene Representations that Refer to the Image. 1335-1342
  
• Marshall F. Tappen, William T. Freeman, Edward H. Adelson:
  Recovering Intrinsic Images from a Single Image. 1343-1350
  
• Nuno Vasconcelos:
  Feature Selection by Maximum Marginal Diversity. 1351-1358
  
• Max Welling, Geoffrey E. Hinton, Simon Osindero:
  Learning Sparse Topographic Representations with Products of Student-t Distributions. 1359-1366
  
• Yan Karklin, Michael S. Lewicki:
  A Model for Learning Variance Components of Natural Images. 1367-1374
  
• Barbara Caputo, Gyuri Dorkó:
  How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick. 1375-1382
  
• Stella X. Yu, Ralph Gross, Jianbo Shi:
  Concurrent Object Recognition and Segmentation by Graph Partitioning. 1383-1390
  
• Christopher K. I. Williams, Michalis K. Titsias:
  Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. 1391-1398
  
• Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser:
  Identity Uncertainty and Citation Matching. 1401-1408
  
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard A. Müller:
  The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging. 1409-1416
  
• Christina S. Leslie, Eleazar Eskin, Jason Weston, William Stafford Noble:
  Mismatch String Kernels for SVM Protein Classification. 1417-1424
  
• Jean-Philippe Vert, Minoru Kanehisa:
  Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA. 1425-1432
  
• Rubén Morales-Menéndez, Nando de Freitas, David Poole:
  Real-Time Monitoring of Complex Industrial Processes with Particle Filters. 1433-1440
  
• Dmitry Pavlov, David M. Pennock:
  A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. 1441-1448
  
• Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi:
  Prediction of Protein Topologies Using Generalized IOHMMS and RNNs. 1449-1456
  
• Chen Yanover, Yair Weiss:
  Approximate Inference and Protein-Folding. 1457-1464
  
• Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari:
  Adaptive Caching by Refetching. 1465-1472
  
• Alexei Vinokourov, John Shawe-Taylor, Nello Cristianini:
  Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis. 1473-1480
  
• William W. Cohen:
  Improving a Page Classifier with Anchor Extraction and Link Analysis. 1481-1488
  
• Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell:
  A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. 1489-1496
  
• Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath:
  Learning to Classify Galaxy Shapes Using the EM Algorithm. 1497-1504
  
• Eric Brochu, Nando de Freitas:
  "Name That Song!" A Probabilistic Approach to Querying on Music and Text. 1505-1512
  
• Matthew G. Snover, Michael R. Brent:
  A Probabilistic Model for Learning Concatenative Morphology. 1513-1520
  
• Auke Jan Ijspeert, Jun Nakanishi, Stefan Schaal:
  Learning Attractor Landscapes for Learning Motor Primitives. 1523-1530
  
• Bernd Porr, Florentin Wörgötter:
  Learning a Forward Model of a Reflex. 1531-1538
  
• Jun Morimoto, Christopher G. Atkeson:
  Minimax Differential Dynamic Programming: An Application to Robust Biped Walking. 1539-1546
  
• Jürgen Schmidhuber:
  Bias-Optimal Incremental Problem Solving. 1547-1546
  
• Pascal Poupart, Craig Boutilier:
  Value-Directed Compression of POMDPs. 1547-1554
  
• Ralf Schoknecht:
  Optimality of Reinforcement Learning Algorithms with Linear Function Approximation. 1555-1562
  
• Maxim Likhachev, Sven Koenig:
  Speeding up the Parti-Game Algorithm. 1563-1570
  
• Xiaofeng Wang, Tuomas Sandholm:
  Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games. 1571-1578
  
• Ralf Schoknecht, Artur Merke:
  Convergent Combinations of Reinforcement Learning with Linear Function Approximation. 1579-1586
  
• Daniela Pucci de Farias, Benjamin Van Roy:
  Approximate Linear Programming for Average-Cost Dynamic Programming. 1587-1594
  
• Theodore J. Perkins, Doina Precup:
  A Convergent Form of Approximate Policy Iteration. 1595-1602
  
• Ronen I. Brafman, Moshe Tennenholtz:
  Efficient Learning Equilibrium. 1603-1610
  
• Christopher G. Atkeson, Jun Morimoto:
  Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach. 1611-1618
  
• Khashayar Rohanimanesh, Sridhar Mahadevan:
  Learning to Take Concurrent Actions. 1619-1626
  
• Michail G. Lagoudakis, Ronald Parr:
  Learning in Zero-Sum Team Markov Games Using Factored Value Functions. 1627-1634
  
• Nicholas Roy, Geoffrey J. Gordon:
  Exponential Family PCA for Belief Compression in POMDPs. 1635-1642
  

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