TUESDAY AM   8:30 Invited -- How Anomalous are Financial Time Series Anomalies?, Andy Lo   9:20 Oral - AA01 Maximum Entropy Discrimination, Tommi Jaakkola, Marina Meila, Tony Jebara   9:40 Oral - AA02 Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology, Yair Weiss, William T. Freeman 10:00 Spotlight  -- AA03 An Analysis of Turbo Decoding with Gaussian Densities, Paat Rusmevichientong, Benjamin Van Roy :03 Spotlight - AA04 Variational Inference for Bayesian Mixture of Factor Analyzers, Zoubin Ghahramani, Matthew J. Beal :06 Spotlight - AA05 Agglomerative Information Bottleneck, Noam Slonim, Naftali Tishby :09 Spotlight - LT01 The Entropy Regularization Information Criterion, Alex J. Smola, John Shawe-Taylor, Bernhard Scholkopf, Robert C. Williamson :12 Spotlight - LT02 Probabilistic Methods for Support Vector Machines, Peter Sollich BREAK 11:00 Oral - CS01 Rules and Similarity in Concept Learning, Joshua B. Tenenbaum 11:20 Oral - VS01 Looking for Sounds: Using Audio-Visual Correlation to Locate Sound Sources, John Hershey, Javier Movellan, Hiroshi Ishiguro 11:40 Oral - VS02 Bayesian Reconstruction of 3D Human Motion from Single-Camera Video, Nicholas R. Howe, Michael E. Leventon, William T. Freeman ============================================== TUESDAY PM 14:00 Invited -- Sound Processing for Cochlear Implants: Rationale, Implementation and Patient Performance, Don Eddington 14:50 Oral - CN01 Policy Gradient Methods for Reinforcement Learning with Function Approximation, Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour 15:10 Oral - CN02 Actor-Critic Algorithms, Vijay R. Konda, John N. Tsitsiklis 15:30 Spotlight - NS01 Predictive Sequence Learning in Recurrent Neocortical Circuits, R.P.N. Rao, T.J. Sejnowski :33 Spotlight - CS02 Acquisition in Autoshaping, Sham Kakade, Peter Dayan :36 Spotlight - CS03 Perceptual Organization Based on Temporal Dynamics, Xiuwen Liu, DeLiang L. Wang :39 Spotlight - SP01 An Oscillatory Correlation Framework for Computational Auditory Scene Analysis, Guy J. Brown, DeLiang L. Wang :42 Spotlight - CN03 Learning Factored Representations for Partially Observable Markov Decision Processes, Brian Sallans :45 Spotlight - CN04 Efficient Policy Search via Density Estimation, Andrew Y. Ng, Ronald Parr, Daphne Koller BREAK 16:15 Oral - IM01 An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control, Oliver Landolt, Steve Gyger 16:35 Oral - IM02 The Parallel Problems Server: An Interactive Tool for Large Scale Machine Learning, Charles Lee Isbell, Jr., Parry Husbands 16:55 Oral - SP02 Bayesian Modelling of fMRI Time Series, Pedro A.d.F.R. Hojen-Sorensen, Lars K. Hansen, Carl Edward Rasmussen TUESDAY EVENING POSTERS IM01 An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control, Oliver Landolt, Steve Gyger IM02 The Parallel Problems Server: An Interactive Tool for Large Scale Machine Learning, Charles Lee Isbell, Jr., Parry Husbands IM03 An Analog VLSI Model of Periodicity Extraction, Andre van Schaik IM04 Bifurcation Analysis of a Silicon Neuron,  Girish N. Patel, Gennady S. Cymbalyuk, Ronald L. Calabrese, Stephen P. DeWeerth IM05 A Winner-Take-All Circuit with Controllable Soft Max Property, Shih-Chii Liu IM06 A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion, Girish N. Patel, Edgar A. Brown, Stephen P. DeWeerth CN01 Policy Gradient Methods for Reinforcement Learning with Function Approximation, Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour CN02 Actor-Critic Algorithms, Vijay R. Konda, John N. Tsitsiklis CN03 Learning Factored Representations for Partially Observable Markov Decision Processes, Brian Sallans CN04 Efficient Policy Search via Density Estimation, Andrew Y. Ng, Ronald Parr, Daphne Koller CN05 Coastal Navigation with Mobile Robots,  Nicholas Roy, Sebastian Thrun CN06 Bayesian Map Learning in Dynamic Environments, Kevin P. Murphy CS01 Rules and Similarity in Concept Learning, Joshua B. Tenenbaum CS02 Acquisition in Autoshaping, Sham Kakade, Peter Dayan CS03 Perceptual Organization Based on Temporal Dynamics, Xiuwen Liu, DeLiang L. Wang CS04 Recognizing Evoked Potentials in a Virtual Environment, Jessica D. Bayliss, Dana H. Ballard CS05 A Neurodynamical Approach to Visual Attention, Gustavo Deco, Josef Zihl CS06 Robust Recognition of Noisy and Superimposed Patterns via Selective Attention, Soo-Young Lee, Michael C. Mozer LT01 The Entropy Regularization Information Criterion, Alex J. Smola, John Shawe-Taylor, Bernhard Scholkopf, Robert C. Williamson LT02 Probabilistic Methods for Support Vector Machines, Peter Sollich LT03 Uniqueness of the SVM Solution, Christopher J.C. Burges, David J. Crisp LT04 Regular and Irregular Gallager-Type Error-Correcting Codes, Y. Kabashima, T. Murayama, D. Saad, R. Vicente LT05 Algebraic Analysis for Non-Regular Learning Machines, Sumio Watanabe LT06 Statistical Dynamics of Batch Learning, S. Li, K. Y. Michael Wong LT07 On the Computational Power of Winner-Take-All, Wolfgang Maass LT08 Resonance in Stochastic Neuron Model with Delayed Interaction, Toru Ohira, Yuzuru Sato, Jack D. Cowan LT09 Noisy Neural Networks and Generalizations, Hava T. Siegelmann, Alex Roitershtein, Asa Ben-Hur LT10 Bayesian Averaging is Well-Temperated, Lars Kai Hansen LT11 Inference for the Generalization Error, Claude Nadeau, Yoshua Bengio LT12 Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems, Liqing Zhang, Shunichi Amari, Andrzej Cichocki NS01 Predictive Sequence Learning in Recurrent Neocortical Circuits, R.P.N. Rao, T.J. Sejnowski NS02 Neural Representation of Multi-Dimensional Stimuli, Christian W. Eurich, Stefan D. Wilke, Helmut Schwegler NS03 Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration, Panayiota Poirazi, Bartlett W. Mel NS04 Wiring Optimization in the Brain, Dmitri B. Chklovskii, Charles F. Stevens NS05 LTD Facilitates Learning in a Noisy Environment, Paul Munro, Gerardina Hernandez NS06 Can V1 Mechanisms Account for Figure-Ground and Medial Axis Effects?,  Zhaoping Li TUESDAY EVENING POSTERS (CONT'D) NS07 Effective Learning Requires Neuronal Remodeling of Hebbian Synapses, Gal Chechik, Isaac Meilijson, Eytan Ruppin NS08 A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks, Alfonso Renart, Nestor Parga and Edmund T. Rolls AP01 Application of Blind Separation of Sources to Optical Recording of Brain Activity, Holger Schoner, Martin Stetter, Ingo Schiessl, John E.W. Mayhew, Jennifer S. Lund, Niall McLoughlin, Klaus Obermayer AP02 Unmixing Hyperspectral Data, Lucas Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Muller AP03 Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting, Yuansong Liao, John Moody AP04 From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data, Eric Mjolsness, Tobias Mann, Rebecca Castano, Barbara Wold AP05 Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization, Thomas Hofmann AP06 Generalized Model Selection for Unsupervised Learning in High Dimensions, Shivakumar Vaithyanathan, Byron Dom AP07 Learning Informative Statistics: A Nonparametric Approach, John W. Fisher III, Paul A. Viola, Alexander T. Ihler VS01 Looking for Sounds: Using Audio-Visual Correlation to Locate Sound Sources, John Hershey, Javier Movellan, Hiroshi Ishiguro VS02 Bayesian Reconstruction of 3D Human Motion from Single-Camera Video, Nicholas R. Howe, Michael E. Leventon, William T. Freeman VS03 Hierarchical Image Probability (HIP) Models, Lucas Parra, Clay D. Spence VS04 A SNoW-Based Face Detector, Dan Roth, Ming-Hsuan Yang, Narendra Ahuja SP01 An Oscillatory Correlation Framework for Computational Auditory Scene Analysis, Guy J. Brown, DeLiang L. Wang SP02 Bayesian Modelling of fMRI Time Series, Pedro A.d.F.R. Hojen-Sorensen, Lars K. Hansen, Carl Edward Rasmussen SP03 Online Independent Component Analysis with Local Learning Rate Adaptation, Nicol N. Schraudolph, Xavier Giannakopoulos SP04 Spectral Cues in Human Sound Localization, Craig T. Jin, Anna Corderoy, Simon Carlile, Andre van Schaik AA01 Maximum Entropy Discrimination, Tommi Jaakkola, Marina Meila, Tony Jebara AA02 Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology, Yair Weiss, William T. Freeman AA03 An Analysis of Turbo Decoding with Gaussian Densities, Paat Rusmevichientong, Van Roy AA04 Variational Inference for Bayesian Mixture of Factor Analysers, Zoubin Ghahramani, Matthew J. Beal AA05 Agglomerative Information Bottleneck, Noam Slonim, Naftali Tishby AA06 Differentiating Functions of the Jacobian with Respect to the Weights, Gary William Flake, Barak A. Pearlmutter AA07 Bayesian Transduction, Thore Graepel, Ralf Herbrich, Klaus Obermayer AA08 Dynamic Graphical Models for Independent Factor Analysis, Hagai Attias AA09 Probabilistic Hierarchical Clustering, Christopher K. I. Williams AA10 The Nonnegative Boltzmann Machine, Oliver B. Downs, David J.C. MacKay, Daniel D. Lee AA11 Building Predictive Models from Spatial Representations of Symbolic Sequences, Peter Tino, Georg Dorffner TUESDAY EVENING POSTERS (CONT'D) AA12 Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints, Miguel A. Carreira-Perpinan AA13 Turbo Factor Analysis, Brendan J. Frey AA14 Transductive Inference for Estimating Values of Functions, Olivier Chapelle, Vladimir Vapnik, Jason Weston AA15 Data Visualization and Feature Selection: New Algorithms for Nongaussian Data, Howard Hua Yang, John Moody AA16 Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks, Mike Schuster AA17 RoBoost: Ensemble Learning in the Presence of  Outliers, Gunnar Ratsch, Bernhard Scholkopf, Alex Smola, Klaus-Robert Muller, Sebastian Mika AA18 Learning to Parse Images, Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh AA19 Predictive Approaches for Choosing Hyperparameters in Gaussian Processes, S. Sundararajan, S. Sathiya Keerthi AA20 Robust Full Bayesian Methods for Neural Networks, Christophe Andrieu, Joao FG de Freitas, Arnaud Doucet AA21 A Multi-Class Linear Learning Algorithm, Chris Mesterharm AA22 Bayesian Network Induction via Local Neighborhoods, Dimitris Margaritis, Sebastian Thrun AA23 Nonlinear Discriminant Analysis Using Kernel Functions, Volker Roth, Volker Steinhage WEDNESDAY  AM   8:30 Invited -- Spatio-Temporal Computations in Biological Neural Nets, Bard Ermentrout   9:20 Oral - LT13 Potential Boosters ?, Nigel P. Duffy, David Helmbold   9:40 Oral - AA24 Boosting Algorithms as Gradient Descent, Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean 10:00 Spotlight - NS09 Optimal Sizes of Dendritic and Axonal Arbors , Dmitri B. Chklovskii :03 Spotlight - NS10 Population Decoding Based on an Unfaithful Model, S. Wu, H. Nakahara, N. Murata, S. Amari :06 Spotlight - AP08 Image Recognition in Context: Application to Microscopic Urinalysis, Xubo Song, Joseph Sill, Harvey Kasdan :09 Spotlight - LT14 Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks , Michael Schmitt :12 Spotlight  -- LT15 Efficient Approaches to Gaussian Process Classification, Lehel Csato, Ernest Fokoue, Manfred Opper, Bernhard Schottky, Ole Winther :15 Spotlight - LT16 Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers, Charles W.H. Mace, Anthony C.C. Coolen   BREAK 11:00 Oral - LT17 Mixture Density Estimation, Jonathan Q. Li, Andrew R. Barron 11:20 Oral - LT18 Learning Structure of Latent Variable Models by Variational Bayes, Hagai Attias 11:40 Oral - LT19 Model Selection in Clustering by Uniform Convergence Bounds, Joachim M. Buhmann, Marcus Held ============================================== WEDNESDAY PM 14:00 Invited -- Deconstructing Synchrony, J. Anthony Movshon 14:50 Oral - NS11 Information Capacity and Robustness of Stochastic Neuron Models, Elad Schneidman, Idan Segev, Naftali Tishby 15:10 Oral - NS12 Recurrent Cortical Competition: Strengthen or Weaken?, Peter Adorjan, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer 15:30 Spotlight - VS05 Independent Subspace Analysis and Emergence of Complex Cell Properties from Natural Images, Aapo Hyvarinen, Patrik Hoyer :33 Spotlight - AA25 Robust Neural Network Regression for Offline and Online Learning, Thomas Briegel, Volker Tresp :36 Spotlight - AA26 Dual Estimation and the Unscented Transformation, Eric A. Wan, Rudolph van der  Merwe, Alex T.  Nelson :39 Spotlight - AA27 Large Margin DAGs for Multiclass Classification, John C. Platt, Nello Cristianini, John Shawe-Taylor :42 Spotlight - AA28 Support Vector Method for Multivariant Density Estimation, Vladimir N. Vapnik, Sayan Mukherjee BREAK 16:15 Oral - NS13 Spiking Belief Networks, Geoffrey E. Hinton, Andrew D. Brown 16:35 Oral - NS14 Spike-Based Learning Rules and Stabilization of Persistent Neural Activity, Xiaohui Xie, H. Sebastian Seung WEDNESDAY EVENING POSTERS CN07 Monte Carlo POMDPs, Sebastian Thrun CN08 State Abstraction in MAXQ Hierarchical Reinforcement Learning, Thomas G. Dietterich CN09 Neural Network Based Model Predictive Control, Stephen Piche, Jim Keeler, Greg Martin, Gene Boe, Doug Johnson, Mark Gerules CN10 An Environment Model for Nonstationary Reinforcement Learning, Samuel P. M. Choi, Dit-Yan Yeung, Nevin L. Zhang CN11 Approximate Planning in Large POMDPs via Reusable Trajectories, Michael Kearns, Yishay Mansour, Andrew Y. Ng CN12 Reinforcement Learning Using Approximate Belief States, Andres Rodriguez, Ronald Parr, Daphne Koller CS07 Connectionist Models of Perception and the Morton-Massaro Law, Javier R. Movellan, James L. McClelland CS08 Evolving Learnable Languages, Bradley Tonkes, Alan Blair, Janet Wiles CS09 Graded Grammaticality in Prediction Fractal Machines, Shan Parfitt, Peter Tino, Georg Dorffner CS10 Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information, Thea B. Ghiselli-Crippa, Paul W. Munro CS11 Learning Statistically Neutral Tasks Without Expert Guidance, Ton Weijters, Antal van den Bosch, Eric Postma CS12 Localist Attractor Networks, Richard S. Zemel, Michael C. Mozer LT13 Potential Boosters ?, Nigel P. Duffy, David Helmbold LT14 Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks, Michael Schmitt LT15 Efficient Approaches to Gaussian Process Classification, Lehel Csato, Ernest Fokoue, Manfred Opper, Bernhard Schottky, Ole Winther LT16 Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers, Charles W.H. Mace, Anthony C.C. Coolen LT17 Mixture Density Estimation, Jonathan Q. Li, Andrew R. Barron LT18 Learning Structure of Latent Variable Models by Variational Bayes, Hagai Attias LT19 Model Selection in Clustering by Uniform Convergence Bounds, Joachim M. Buhmann, Marcus Held LT20 A Geometric Interpretation of ?-SVM Classifiers, David J. Crisp, Christopher J.C. Burges LT21 Model Selection for Support Vector Machines, Olivier Chapelle, Vladimir Vapnik LT22 Understanding Stepwise Generalization of Support Vector Machines: A Toy Model, Sebastian Risau-Gusman, Mirta B. Gordon LT23 Boosting with Multi-Way Branching in Decision Trees, Yishay Mansour, David McAllester LT24 Some Theoretical Results Concerning the Convergence of Composition of Regularized Linear Functions, Tong Zhang NS09 Optimal Sizes of Dendritic and Axonal Arbors , Dmitri B. Chklovskii NS10 Population Decoding Based on an Unfaithful Model, S. Wu, H. Nakahara, N. Murata, S. Amari NS11 Information Capacity and Robustness of Stochastic Neuron Models, Elad Schneidman, Idan Segev, Naftali Tishby NS12 Recurrent Cortical Competition: Strengthen or Weaken?, Peter Adorjan, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer NS13 Spiking Belief Networks, Geoffrey E. Hinton, Andrew D. Brown NS14 Spike-Based Learning Rules and Stabilization of Persistent Neural Activity, Xiaohui Xie, H. Sebastian Seung NS15 Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly, David Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin NS16 Channel Noise in Excitable Neuronal Membranes, Amit Manwani, Peter N. Steinmetz, Christof  Koch WEDNESDAY EVENING POSTERS (CONT'D) NS17 An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task, Akaysha C. Tang, Barak A. Pearlmutter, Michael Zibulevsky, Tim Hely, Michael Weisend AP08 Image Recognition in Context: Application to Microscopic Urinalysis, Xubo Song, Joseph Sill, Harvey Kasdan AP09 Image Representations for Facial Action Coding, Marian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski AP10 Kirchoff Law Markov Fields for Analog Circuit Design, Richard M. Golden AP11 Low Power Wireless Communication via Reinforcement Learning, Timothy X Brown AP12 Reinforcement Learning for Spoken Dialogue Systems, Satinder Singh, Michael Kearns, Diane Litman, Marilyn Walker AP13 Robust Learning of Chaotic Attractors, Rembrandt Bakker, Jaap C. Schouten, Marc-Olivier Coppens, Floris Takens, C. Lee Giles, Cor M. van den Bleek (THE FOLLOWING TWO AP POSTERS CORRESPOND TO THURSDAY ORAL PRESENTATIONS) AP14 Churn Reduction in the Wireless Industry, Michael Mozer, Richard Wolniewicz, Eric Johnson, Howard Kaushansky AP15 Learning from User Feedback in Image Retrieval Systems, Nuno Vasconcelos, Andrew Lippman VS05 Independent Subspace Analysis and Emergence of Complex Cell Properties from Natural Images, Aapo Hyvarinen, Patrik Hoyer VS06 Learning Sparse Codes with a Mixture-Of-Gaussians Prior, Bruno A. Olshausen, K. Jarrod Millman VS07 Scale Mixtures of Gaussians and the Statistics of Natural Images, Martin J. Wainwright, Eero P. Simoncelli VS08 An Information-Theoretic Framework for Understanding Saccadic Behaviors, Tai Sing Lee, Stella X. Yu VS09 A Generative Model for Visual Cue Combination, Zhiyong Yang, Richard S Zemel SP05 Neural System Model of Human Sound Localization, Craig T. Jin, Simon Carlile SP06 Constrained Hidden Markov Models, Sam Roweis SP07 Speech Modelling Using Subspace and EM Techniques, Gavin A. Smith, Joao F. G. de Freitas, Anthony J. Robinson, Mahesan Niranjan SP08 Search for Information Bearing Components in Speech, Howard H. Yang, Hynek Hermansky SP09 Broadband Direction-Of-Arrival Estimation Based On Second Order Statistics, Justinian Rosca, Joseph O Ruanaidh, Alexander N. Jourjine, Scott Rickard AA24 Boosting Algorithms as Gradient Descent, Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean AA25 Robust Neural Network Regression for Offline and Online Learning, Thomas Briegel, Volker Tresp AA26 Dual Estimation and the Unscented Transformation, Eric A. Wan, Rudolph van der  Merwe, Alex T.  Nelson AA27 Large Margin DAGs for Multiclass Classification, John C. Platt, Nello Cristianini, John Shawe-Taylor AA28 Support Vector Method for Multivariant Density Estimation, Vladimir N. Vapnik, Sayan Mukherjee AA29 Invariant Feature Extraction and Classification in Kernel Spaces, Sebastian Mika, Gunnar Ratsch, Jason Weston, Bernhard Scholkopf, Alex Smola, Klaus-Robert Muller AA30 Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers, Matthias Seeger AA31 The Relaxed Online Maximum Margin Algorithm, Yi Li, Philip M. Long AA32 Topographic Transformation as a Discrete Latent Variable, Nebojsa Jojic, Brendan J. Frey AA33 The Infinite Gaussian Mixture Model, Carl Edward Rasmussen AA34 Optimal Kernel Shapes for Local Linear Regression, Dirk Ormoneit, Trevor Hastie AA35 An Improved Decomposition Algorithm for Regression Support Vector Machines, Pavel Laskov AA36 SV Estimation of a Distribution's Support, Bernhard Scholkopf, Robert C. Williamson, Alex Smola, John Shawe-Taylor AA37 Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks, Masashi Sugiyama, Hidemitsu Ogawa AA38 On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling, Peter Sykacek AA39 Greedy Importance Sampling, Dale Schuurmans AA40 Leveraged Vector Machines, Yoram Singer AA41 Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks, David Barber, Peter Sollich AA42 Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks, Yoshua Bengio, Samy Bengio (THE FOLLOWING AA POSTERS CORRESPOND TO THURSDAY ORAL PRESENTATIONS) AA43 Algorithms for Independent Components Analysis and Higher Order Statistics, Daniel D. Lee, Uri Rokni, Haim Sompolinsky AA44 Manifold Stochastic Dynamics for Bayesian Learning, Mark Zlochin, Yoram Baram AA45 The Relevance Vector Machine, Michael E. Tipping AA46 Approximate Inference Algorithms for Two-Layer Bayesian Networks, Andrew Y. Ng, Michael I. Jordan THURSDAY   8:30 Oral - AA43 Algorithms for Independent Components Analysis and Higher Order Statistics, Daniel D. Lee, Uri Rokni, Haim Sompolinsky   8:50 Oral - AA44 Manifold Stochastic Dynamics for Bayesian Learning, Mark Zlochin, Yoram Baram   9:10 Oral - AA45 The Relevance Vector Machine, Michael E. Tipping   9:30 Oral -- AA46 Approximate Inference Algorithms for Two-Layer Bayesian Networks, Andrew Y. Ng, Michael I. Jordan BREAK 10:30 Oral - AP14 Churn Reduction in the Wireless Industry, Michael Mozer, Richard Wolniewicz, Eric Johnson, Howard Kaushansky 10:50 Oral - AP15 Learning from User Feedback in Image Retrieval Systems, Nuno Vasconcelos, Andrew Lippman 11:10 Invited -- Animation of Human Motion, Jessica Hodgins