Program		Neural Information Processing Systems: Natural and Synthetic
						Here is the NIPS*2002 program. Click for the full printed program [pdf].
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				For Authors and Presenters
						Tuesday Posters AA01 Carl Edward Rasmussen and Zoubin Ghahramani: Bayesian Monte Carlo AA02 Bin Wu, K. Y. Michael Wong, and David Bodoff: Mean Field Approach to a Probabilistic Model in Information Retrieval AA03 Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell: Distance Metric Learning, with application to Clustering with side-information AA04 Gunter Raetsch, Alexander Smola, and Sebastian Mika: Adapting Codes und Embeddings for Polychotomies AA05 Glenn M. Fung, Olvi L. Mangasarian, and Jude W. Shavlik: Knowledge-Based Support Vector Machine Classifiers AA06 Agathe Girard, Carl Edward Rasmussen, and Roderick Murray-Smith: Multiple-step ahead prediction for non linear dynamic systems -- A Gaussian Process treatment with propagation of the uncertainty AA07 Koby Crammer, Joseph Keshet, and Yoram Singer: Kernel Design using Boosting AA08 Sepp Hochreiter, Michael C. Mozer, and Klaus Obermayer: Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems AA09 Yves Grandvalet and Stephane Canu: Adaptive Scaling for Feature Selection in SVMs AA10 Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann: Support Vector Machines for Multiple- Instance Learning AA11 S.V.N. Vishwanathan and Alexander J. Smola: Fast Kernels for String and Tree Matching AA12 Geoffrey J. Gordon: Generalized^2 Linear^2 Models AA13 Olivier Chapelle, Jason Weston, and Bernhard Schoelkopf: Cluster Kernels for Semi-Supervised Learning AA14 Herbert Jaeger: Adaptive nonlinear system identification with echo state networks AA15 Corinna Cortes, Patrick Haffner, and Mehryar Mohri: Rational Kernels AA16 Matthias Seeger, Neil Lawrence, and Ralf Herbrich: Fast Sparse Gaussian Process Methods: The Informative Vector Machine AA17 Tilman Lange, Mikio Braun, Volker Roth, and Joachim Buhmann: Stability-Based Model Selection AA18 Martin H. Law, Anil K. Jain, and Mario A. T. Figueiredo: Feature Saliency in Mixture-Based Clustering AA19 Craig Saunders, John Shawe-Taylor, and Alexei Vinokourov: String Kernels, Fisher Kernels and Finite State Automata AA20 Saharon Rosset, and Eran Segal: Boosting Density Estimation AA21 Trevor Hastie and Robert Tibshirani: Independent Components Analysis through Product Density Estimation AA22 Jaz Kandola, John Shawe-Taylor, and Nello Cristianini: Learning Semantic Similarity AA23 Max Welling, Richard Zemel, and Geoffrey Hinton: Self Supervised Boosting AA24 Alexander G. Gray, Bernd Fischer, Johann Schumann, and Wray Buntine: Automatic Derivation of Statistical Algorithms: The EM Family and Beyond AA25 Balazs Kegl: Intrinsic Dimension Estimation Using Packing Numbers AA26 Chakra Chennubhotla and Allan Jepson: Half-Lives of EigenFlows for Spectral Clustering AA27 Harald Steck and Tommi Jaakkola: On the Dirichlet Prior and Bayesian Regularization AA28 Vin de Silva and Joshua B. Tenenbaum: Global versus local approaches to nonlinear dimensionality reduction. AA29 David Barber: Dynamic Bayesian Networks with Deterministic Latent Tables AA30 Naonori Ueda and Kazumi Saito: Parametric mixture models for multi-labeled text AA31 Koji Tsuda, Motoaki Kawanabe, and Klaus-Robert Mueller: Clustering with the Fisher Score AA32 Peter Sykacek and Stephen Roberts: Adaptive classification by variational Kalman filtering AA33 Baback Moghaddam and Gregory Shakhnarovich: Boosted Dyadic Kernel Discriminants AA34 Finnegan Southey, Dale Schuurmans, and Ali Ghodsi Boushehri: Regularized greedy importance sampling AA35 Elzbieta Pekalska, David M.J. Tax, and Robert P.W. Duin: One-class LP classifier for dissimilarity representations AA36 Thomas Strohmann and Gregory Z. Grudic: A Formulation for Minimax Probability Machine Regression AP01 Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, and Ilya Shpitser: Identity Uncertainty and Citation Matching AP02 Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard Muller, and Ingolf Mesecke-von Rheinbaben: The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging AP03 Christina Leslie, Eleazar Eskin, Jason Weston, and William Stafford Noble: Mismatch String Kernels for SVM Protein Classification AP04 Jean-Philippe Vert and Minoru Kanehisa: Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA AP05 Ruben Morales-Menendez, Nando de Freitas, and David Poole: Real-time monitoring of complex industrial processes with particle filters AP06 Dmitry Y. Pavlov and David M. Pennock: A Maximum Entropy Approach To Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains AP07 Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, and Paolo Frasconi: Prediction of Protein Topologies Using GIOHMMs and GRNNs CN01 Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal: Learning Attractor Landscapes for Learning Motor Primitives CN02 Bernd Porr and Florentin Woergoetter: Learning a forward model of a reflex CN03 Jun Morimoto and Christopher Atkeson: Minimax Differential Dynamic Programming:An Application to Robust Biped Walking CN04 Juergen Schmidhuber: Bias-Optimal Incremental Problem Solving CN05 Pascal Poupart and Craig Boutilier: Value-directed Compression of POMDPs CN06 Ralf Schoknecht: Optimality of Reinforcement Learning Algorithms with Linear Function Approximation CN07 Maxim Likhachev and Sven Koenig: Speeding up the Parti-Game Algorithm CN08 XiaoFeng Wang and Tuomas Sandholm: Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games CS01 Dan Klein and Christopher D. Manning: Fast Exact Inference with a Factored Model for Natural Language Parsing CS02 Thomas L. Griffiths and Mark Steyvers: Prediction and semantic association CS03 Szabolcs Kali and Peter Dayan: Replay, Repair, and Consolidation CS04 Emanuel Todorov and Michael I. Jordan: A Minimal Intervention Principle for Coordinated Movement CS05 David Fass and Jacob Feldman: Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories CS06 Joshua B. Tenenbaum and Thomas L. Griffiths: Theory-based causal inference CS07 Willem Zuidema: How the poverty of stimulus solves the poverty of stimulus IM01 A.R.S. Romariz and K. Wagner: Optoelectronic Implementation of a Fitzugh-Nagumo neural model IM02 Shih-Chii Liu, Malte Boegerhausen, and Pascal Suter: Circuit Model of Short-Term Synaptic Dynamics IM03 David Hsu, Seth Bridges, Miguel Figueroa, and Chris Diorio: Adaptive Quantization and Density Estimation in Silicon IM04 Giacomo Indiveri: Circuits for bistable spike-timing-dependent plasticity neuromorphic VLSI synapses IM05 R. Carmona, F. Jimenez-Garrido, R. Dominguez-Castro, S. Espejo, and A. Rodriguez-Vazquez: Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip IM06 P. Meinicke, M. Kaper, F. Hoppe, M. Heumann, and H. Ritter: Improving Transfer Rates in Brain Computer Interfacing: a Case Study LT01 Ron Meir and Tong Zhang: Data-Dependent Bounds for Bayesian Mixture Methods LT02 Dorthe Malzahn and Manfred Opper: A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages LT03 Noam Slonim and Yair Weiss: Maximum Likelihood and the Information Bottleneck LT04 Tom Heskes: Stable fixed points of loopy belief propagation are minima of the Bethe free energy LT05 David McAllester and Luis Ortiz: Concentration Inequalities for the Missing Mass and Histogram Rule Error LT06 Clayton Scott and Robert Nowak: Dyadic Classification Trees via Structural Risk Minimization LT07 John Shawe-Taylor and Christopher K. I. Williams: The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum LT08 John Lafferty and Guy Lebanon: Information Diffusion Kernels LT09 J. L. Shapiro: Scaling of Probability-based Optimization Algorithms LT10 Sumio Watanabe Shun-ichi Amari: The effect of singularities in a learning machine when the true parameters do not lie on such singularities LT11 Olivier Bosquet and Daniel J.L. Herrmann: On the Complexity of Learning the Kernel Matrix NS01 Michael R. DeWeese and Anthony M. Zador: Binary coding in auditory cortex NS02 Maneesh Sahani and Jennifer Linden: How linear are auditory cortical responses? NS03 W. Wu, M. J. Black, Y. Gao, E. Bienenstock, M. Serruya, A. Shaikhouni, and J. P. Donoghue: Neural Decoding of Cursor Motion using a Kalman Filter NS04 Lavi Shpigelman, Yoram Singer, Rony Paz, and Eilon Vaadia: Spikernels: Embedding Spiking Neurons in Inner-Product Spaces NS05 Christian K. Machens, Michael Wehr, and Anthony M. Zador: Spectro-temporal receptive fields of subthreshold responses in auditory cortex NS06 Jarmo Hurri and Aapo Hyvarinen: Temporal Coherence, Natural Image Sequences, and the Visual Cortex NS07 David Barber: Learning in Spiking Neural Assemblies NS08 Angela Yu and Peter Dayan: Expected and Unexpected Uncertainty: ACh and NE in the Neocortex NS09 Aaron J. Gruber, Sara A. Solla, and James C. Houk: Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons NS10 Liam Paninski: Convergence properties of spike-triggered analysis techniques NS11 Dmitri B. Chklovskii and Armen Stepanyants: Branching Law for Axons NS12 Matthias Bethge, David Rotermund, and Klaus Pawelzik: Binary tuning is optimal for neural rate coding with high temporal resolution NS13 Elad Schneidman, William Bialek, and Michael J. Berry: An information theoretic approach to the functional classification of neurons SP01 Shantanu Chakrabartty and Gert Cauwenberghs: Forward-Decoding Kernel-Based Phone Sequence Recognition SP02 Gil-Jin Jang and Te-Won Lee: A Probabilistic Approach to Single Channel Blind Signal Separation SP03 Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, and Yann LeCun: Real time voice processing with audiovisual feedback: toward autonomous agents with perfect pitch SP04 Sachin S. Kajarekar and Hynek Hermansky: Analysis of Information in Speech using Results of MANOVA SP05 Patrick J. Wolfe and Simon J. Godsill: Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement VS01 Anat Levin, Assaf Zomet, and Yair Weiss: Learning to Perceive Transparency from the Statistics of Natural Scenes VS02 David R. Martin, Charless C. Fowlkes, and Jitendra Malik: Learning to Detect Natural Image Boundaries Using Brightness and Texture VS03 Anitha Kannan, Nebojsa Jojic, and Brendan Frey: Fast transformation-invariant component analysis VS04 M.S. Bartlett, G. Littlewort, B. Braathen, T.J. Sejnowski, and J.R. Movellan: An Approach to Automatic Analysis of Spontaneous Facial Expressions VS05 Michael E Tipping and Christopher M Bishop: Bayesian Image Super-resolution VS06 David B. Grimes and Rajesh P. N. Rao: A bilinear model for sparse coding VS07 Amos Storkey: Dynamic Structure Super-Resolution VS08 Kinh Tieu and Erik Miller: Unsupervised Color Constancy VS09 Leonid Taycher, John W. Fisher, and Trevor Darrell: Recovering Articulated Model Topology from Observed Rigid Motion VS10 Matthias O. Franz and Javaan S. Chahl: Optimal linear estimation of self-motion - a real-world test of a neural model Wednesday Posters AA37 Christopher M. Bishop, David Spiegelhalter, and John Winn: VIBES: A Variational Inference Engine for Bayesian Networks AA38 James Park and Adnan Darwiche: A Differential Semantics for Jointree Algorithms AA39 Sariel Har-Peled, Dan Roth, and Dav Zimak: Constraint Classification for Multiclass Classification and Ranking AA40 Luis E. Ortiz and Michael Kearns: Nash Propagation for Loopy Graphical Games AA41 Dan Pelleg and Andrew Moore: Using Tarjan's Red Rule for Fast Dependency Tree Construction AA42 Martin Wainwright, Tommi Jaakkola, and Alan Willsky: Exact MAP estimates by (hyper)tree agreement AA43 Volker Roth, Julian Laub, Joachim M. Buhmann, and Klaus-Robert Muller: Going metric: Denoising pairwise data AA44 Pascal Vincent and Yoshua Bengio: Manifold Parzen Windows AA45 Geoff Hinton and Sam Roweis: Stochastic Neighbor Embedding AA46 Yee Whye Teh and Sam Roweis: Automatic Alignment of Local Representations AA47 David Cohn: Informed Projections AA48 Gal Chechik and Naftali Tishby: Extracting relevant structures with side information AA49 Kenji Fukumizu, Shotaro Akaho, and Shun-ichi Amari: Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting AA50 Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf, and Vladimir Vapnik: Kernel Dependency Estimation AA51 Kwokleung Chan, Te-Won Lee, and Terrence Sejnowski: Handling Missing Data with Variational Bayesian Learning of ICA AA52 Sepp Hochreiter and Klaus Obermayer: Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers AA53 Rong Jin and Zoubin Ghahramani: Learning with Multiple Labels AA54 Gert R.G. Lanckriet, Laurent El Ghaoui, and Michael I. Jordan: Robust Novelty Detection with Single-Class MPM AA55 Nicholas P. Hughes and David Lowe: Artefactual Structure from Least-squares Multidimensional Scaling AA56 Marina Sokolova, Mario Marchand, Nathalie Japkowicz, and John Shawe-Taylor: The Decision List Machine AA57 Mikhail Belkin and Partha Niyogi: Using Manifold Structure for Partially Labelled Classification AA58 Amnon Shashua and Anat Levin: Taxonomy of Large Margin Principle Algorithms for Ordinal Regression Problems AA59 Ofer Dekel and Yoram Singer: Multiclass Learning by Probabilistic Embeddings AA60 Anton Schwaighofer and Volker Tresp: Transductive and Inductive Methods for Approximate Gaussian Process Regression AA61 Matthew Brand: Charting a manifold AA62 Albert E. Parker, Tomas Gedeon, Alexander G. Dimitrov, and Bryan Roosien: Annealing and the rate distortion problem AA63 Yasemin Altun, Thomas Hofmann, and Mark Johnson: Discriminative Learning for Label Sequences via Boosting AA64 P. Meinicke, T. Twellmann, and H. Ritter: Discriminative Densities from Maximum Contrast Estimation AA65 Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, and HongJiang Zhang: FloatBoost Learning for Classification AA66 Joaquin Quinonero-Candela and Ole Winther: Incremental Gaussian Processes AA67 Francis R. Bach and Michael I. Jordan: Learning Graphical Models with Mercer Kernels AA68 David A. Ross and Richard S. Zemel: Multiple Cause Vector Quantization AA69 Martin Szummer and Tommi Jaakkola: Information Regularization with Partially Labeled Data AA70 Ercan Solak, Roderick Murray-Smith, W. Leithead, D. Leith, and C. Rasmussen: Derivative observations in Gaussian Process models of dynamic systems AA71 Fei Sha, Lawrence K. Saul, and Daniel D. Lee: Multiplicative updates for nonnegative quadratic programming in support vector machines AA72 A. Rahimi and T. Darrell: Location Estimation with a Differential Update Network AA73 Cody Kwok, Dieter Fox, and Marina Meila: Real-time particle filter AP08 Chen Yanover and Yair Weiss: Approximate Inference and Protein-Folding AP09 Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, and Ismail Ari: Adaptive Caching by Refetching AP10 Alexei Vinokourov, John Shawe-Taylor, and Nello Cristianini: Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis AP11 William W. Cohen: Improving A Page Classifier with Anchor Extraction and Link Analysis AP12 Eric P.Xing, Michael I. Jordan, Richard M. Karp, and Stuart Russell: A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences AP13 Sergey Kirshner, Igor Cadez, Padhraic Smyth, and Chandrika Kamath: Learning to Classify Galaxy Shapes using the EM Algorithm AP14 Eric Brochu and Nando de Freitas: Name that Song: A Probabilistic Approach to Querying on Music and Text AP15 Matthew G. Snover and Michael R. Brent: A Probabilistic Model for Learning Concatenative Morphology CN09 Ralf Schoknecht and Artur Merke: Convergent Combinations of Reinforcement Learning with Linear Function Approximation CN10 Daniela Pucci de Farias and Benjamin Van Roy: Approximate Linear programming for Average-Cost Dynamic Programming CN11 Theodore J. Perkins and Doina Precup: A Convergent Form of Approximate Policy Iteration CN12 Ronen Brafman and Moshe Tennenholtz: Efficient Learning Equilibrium CN13 Chris Atkeson and Jun Morimoto: Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach CN14 Khashayar Rohanimanesh and Sridhar Mahadevan: Learning to Take Concurrent Actions CN15 Michail G. Lagoudakis and Ronald Parr: Learning in Multiagent Markov Games CN16 Nicholas Roy and Geoff Gordon: Exponential Family PCA for Belief Compression in POMDPs CS08 Neville E. Sanjana and Joshua B. Tenenbaum: Bayesian Models of Inductive Generalization CS09 Daniel J. Navarro and Michael D. Lee: Combining Dimensions and Features in Similarity-Based Representations CS10 Kenneth J. Malmberg, Rene Zeelenberg, and Richard. M. Shiffrin: Modeling Midazolam's Effect on the Hippocampus and Recognition Memory CS11 X. Danks, T.L. Griffiths, and J. Tenenbaum: Dynamical Causal Learning CS12 Robert A. Jacobs and Melissa Dominguez: Visual Development Aids the Acquisition of Motion Velocity Sensitivities CS13 Nathaniel D. Daw, Aaron C. Courville, and David S. Touretzky: Timing and partial observability in the dopamine system CS14 Zach Solan, Eytan Ruppin, David Horn, and Shimon Edelman: Automatic acquisition and efficient representation of syntactic structures IM07 Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Mueller: Combining Features for BCI IM08 Jakob Heinzle and Alan Stocker: classifying patterns of visual motion - a neuromorphic approach IM09 Terry Elliott, and Jorg Kramer: Developing Topography and Ocular Dominance using two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity IM10 Brian Taba and Kwabena Boahen: Topographic Map Formation by Silicon Growth Cones IM11 R. Jacob Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, and Gert Cauwenberghs: Spike Timing-Dependent Plasticity in the Address Domain IM12 Seth Bridges, Miguel Figueroa, David Hsu, and Chris Diorio: Field-Programmable Learning Arrays LT12 Tatsuto Murayama and Masato Okada: Rate Distortion Function in the Spin Glass State: A Toy Model LT13 Guy Lebanon and John Lafferty: Conditional Models on the Ranking Poset LT14 John Langford and John Shawe-Taylor: PAC-Bayes And Margins LT15 Eric Allender, Sanjeev Arora, Michael Kearns, Christopher Moore, and Alexander Russell: A Note on the Representational Incompatability of Function Approximation and Factored Dynamics LT16 Wim Wiegerinck and Tom Heskes: Fractional Belief Propagation LT17 Jon Kleinberg: An Impossibility Theorem for Clustering LT18 Tong Zhang: Effective dimension and Generalization of Kernel Learning LT19 Koby Crammer, Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby: Margin Analysis of the LVQ Algorithm LT20 Nicolo' Cesa-Bianchi, Alex Conconi, and Claudio Gentile: Margin-based algorithms for information filtering LT21 Cheng Soon Ong, Alexander J. Smola, and Robert C. Williamson: Superkernels NS14 Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, and Terrence Sejnowski: Naive Bayesian Coding of Color in Primary Visual Cortex NS15 Wolfgang Maass, Thomas Natschlaeger, and Henry Markram: A Model for Real-Time Computation in Generic Neural Microcircuits NS16 Peter Dayan, Maneesh Sahani, and Greg Deback: Adaptation and Unsupervised Learning NS17 Pietro Perona, Alex Holub, and Gilles Laurent: A digital antennal lobe for pattern equalization: analysis and design NS18 Michael Eisele and Kenneth D. Miller: Hidden Markov model of cortical synaptic plasticity: Derivation of the learning rule NS19 Luk Chong Yeung, Harel Z. Shouval, and Leon N Cooper: Input Selectivity of Spiking Neurons: Metaplasticity in a Unified Calcium-Dependent Learning Model NS20 Alistair Bray and Dominique Martinez: Kernel-based extraction of Slow Features: Complex cells learn disparity and translation invariance from natural images NS21 Tatyana Sharpee, Nicole C. Rust, and William Bialek: Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals NS22 Arunava Banerjee and Alexandre Pouget: Dynamical constraints on computing with spike timing in the cortex NS23 Patrik O. Hoyer and Aapo Hyvarinen: Interpreting neural response variability as Monte Carlo sampling of the posterior NS24 Alon Fishbach and Bradford J. May: A neural edge-detection model for enhanced auditory sensitivity in modulated noise NS25 Christian W. Eurich: An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli NS26 Maneesh Sahani and Jennifer Linden: Evidence optimization techniques for estimating stimulus-response functions NS27 Duane Q. Nykamp: Reconstructing stimulus-driven neural networks from spike times SP06 Hagai Attias: Source Separation with a Microphone Array using Graphical Models and Subband Filtering SP07 Samy Bengio: An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition SP08 Guoning Hu and DeLiang Wang: Monaural Speech Separation SP09 Udi Ben-Reuven and Yoram Singer: Discriminative Binaural Sound Localization SP10 Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, and Naonori Ueda: Application of the Variational Bayesian Approach to Speech Recognition VS11 Phil A Sallee and Bruno A Olshausen: Learning Sparse Multiscale Image Representations VS12 William T. Freeman and Antonio Torralba: Shape recipes: scene representations that refer to the image VS13 Marshall F Tappen, William T Freeman, and Edward H Adelson: Recovering Intrinsic Images from a Single Image VS14 Nuno Vasconcelos: Feature Selection by Maximum Marginal Diversity VS15 Max Welling, Simon Osindero, and Geoffrey Hinton: Learning Sparse Topographic Representations with Products of Student-t Distributions VS16 Yan Karklin and Michael S. Lewicki: Higher-order structure of natural images VS17 B. Caputo and Gy. Dorko: How to combine color and shape information for 3D object recognition: kernels do the trick VS18 Stella X. Yu, Ralph Gross, and Jianbo Shi: Object Segmentation by Graph Partitioning VS19 Christopher K. I. Williams and Michalis K. Titsias: Learning About Multiple Objects in Images: Factorial Learning without Factorial Search
		Monday
Post-dinner		Conference keynote: Deborah Gordon (introduced by Joshua Tenenbaum)
		Tuesday
8:30-9:20		Invited talk: David Heeger (introduced by Klaus-Robert Mueller) Session chair: Joshua Tenenbaum
9:20- 9:40		VS01: Anat Levin, Assaf Zomet, and Yair Weiss: Learning to Perceive Transparency from the Statistics of Natural Scenes
9:40-10:00		VS02: David R. Martin, Charless C. Fowlkes, and Jitendra Malik: Learning to Detect Natural Image Boundaries Using Brightness and Texture
10:00-10:40		--- coffee break --- Session chair: Marina Meila
10:40-11:00		AA01: Carl Edward Rasmussen and Zoubin Ghahramani: Bayesian Monte Carlo
11:00-11:20		LT01: Ron Meir and Tong Zhang: Data-Dependent Bounds for Bayesian Mixture Methods
11:20-11:40		LT02: Dorthe Malzahn and Manfred Opper: A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages
11:40-12:00		SPOTLIGHTS (10) SP01: Shantanu Chakrabartty and Gert Cauwenberghs: Forward-Decoding Kernel-Based Phone Sequence Recognition SP02: Gil-Jin Jang and Te-Won Lee: A Probabilistic Approach to Single Channel Blind Signal Separation SP03: Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, and Yann LeCun: Real time voice processing with audiovisual feedback: toward autonomous agents with perfect pitch CS01: Dan Klein and Christopher D. Manning: Fast Exact Inference with a Factored Model for Natural Language Parsing CS02: Thomas L. Griffiths and Mark Steyvers: Prediction and semantic association AA02: Bin Wu, K. Y. Michael Wong, and David Bodoff: Mean Field Approach to a Probabilistic Model in Information Retrieval AP01: Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, and Ilya Shpitser: Identity Uncertainty and Citation Matching LT03: Noam Slonim and Yair Weiss: Maximum Likelihood and the Information Bottleneck AA03: Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell: Distance Metric Learning, with application to Clustering with side-information AP02: Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard Muller, and Ingolf Mesecke-von Rheinbaben: The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging
12:00-2:00		--- lunch break --- Session chair: Chris Williams
2:00- 2:20		AA04: Gunter Raetsch, Alexander Smola, and Sebastian Mika: Adapting Codes und Embeddings for Polychotomies
2:20- 2:40		AA05: Glenn M. Fung, Olvi L. Mangasarian, and Jude W. Shavlik: Knowledge-Based Support Vector Machine Classifiers
2:40- 3:00		AP03: Christina Leslie, Eleazar Eskin, Jason Weston, and William Stafford Noble: Mismatch String Kernels for SVM Protein Classification
3:00- 3:20		AA06: Agathe Girard, Carl Edward Rasmussen, and Roderick Murray-Smith: Multiple-step ahead prediction for non linear dynamic systems -- A Gaussian Process treatment with propagation of the uncertainty
3:20- 3:30		SPOTLIGHTS (5) AA07: Koby Crammer, Joseph Keshet, and Yoram Singer: Kernel Design using Boosting AA08: Sepp Hochreiter, Michael C. Mozer, and Klaus Obermayer: Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems AA09: Yves Grandvalet and Stephane Canu: Adaptive Scaling for Feature Selection in SVMs AA10: Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann: Support Vector Machines for Multiple- Instance Learning AA11: S.V.N. Vishwanathan and Alexander J. Smola: Fast Kernels for String and Tree Matching
3:30-3:55		--- coffee break --- Session chair: Geoffrey Gordon
3:55- 4:15		NS01: Michael R. DeWeese and Anthony M. Zador: Binary coding in auditory cortex
4:15- 4:35		NS02: Maneesh Sahani and Jennifer Linden: How linear are auditory cortical responses?
4:35- 4:49		SPOTLIGHTS (7) NS03: W. Wu, M. J. Black, Y. Gao, E. Bienenstock, M. Serruya, A. Shaikhouni, and J. P. Donoghue: Neural Decoding of Cursor Motion using a Kalman Filter NS04: Lavi Shpigelman, Yoram Singer, Rony Paz, and Eilon Vaadia: Spikernels: Embedding Spiking Neurons in Inner-Product Spaces NS05: Christian K. Machens, Michael Wehr, and Anthony M. Zador: Spectro-temporal receptive fields of subthreshold responses in auditory cortex NS06: Jarmo Hurri and Aapo Hyvarinen: Temporal Coherence, Natural Image Sequences, and the Visual Cortex CS03: Szabolcs Kali and Peter Dayan: Replay, Repair, and Consolidation CN01: Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal: Learning Attractor Landscapes for Learning Motor Primitives CN02: Bernd Porr and Florentin Woergoetter: Learning a forward model of a reflex
4:50- 5:10		CN03: Jun Morimoto and Christopher Atkeson: Minimax Differential Dynamic Programming:An Application to Robust Biped Walking
5:10- 5:30		CS04: Emanuel Todorov and Michael I. Jordan: A Minimal Intervention Principle for Coordinated Movement
		Wednesday
8:30- 9:20		Invited talk: Pietro Perona (introduced by Richard Zemel) Session chair: Klaus-Robert Mueller
9:20- 9:40		SPOTLIGHTS (10) AA37: Christopher M. Bishop, David Spiegelhalter, and John Winn: VIBES: A Variational Inference Engine for Bayesian Networks AA38: James Park and Adnan Darwiche: A Differential Semantics for Jointree Algorithms LT12: Tatsuto Murayama and Masato Okada: Rate Distortion Function in the Spin Glass State: A Toy Model AP08: Chen Yanover and Yair Weiss: Approximate Inference and Protein-Folding CS08: Neville E. Sanjana and Joshua B. Tenenbaum: Bayesian Models of Inductive Generalization VS11: Phil A Sallee and Bruno A Olshausen: Learning Sparse Multiscale Image Representations VS12: William T. Freeman and Antonio Torralba: Shape recipes: scene representations that refer to the image IM07: Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Mueller: Combining Features for BCI IM08: Jakob Heinzle and Alan Stocker: classifying patterns of visual motion - a neuromorphic approach IM09: Terry Elliott and Jorg Kramer: Developing Topography and Ocular Dominance using two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity
9:40-10:00		IM10: Brian Taba and Kwabena Boahen: Topographic Map Formation by Silicon Growth Cones
10:00-10:40		--- coffee break --- Session chair: Eero Simoncelli
10:40-11:00		VS13: Marshall F Tappen, William T Freeman, and Edward H Adelson: Recovering Intrinsic Images from a Single Image
11:00-11:20		NS14: Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, and Terrence Sejnowski: Naive Bayesian Coding of Color in Primary Visual Cortex
11:20-11:40		NS15: Wolfgang Maass, Thomas Natschlaeger, and Henry Markram: A Model for Real-Time Computation in Generic Neural Microcircuits
11:40-12:00		SPOTLIGHTS (10) AA39: Sariel Har-Peled, Dan Roth, and Dav Zimak: Constraint Classification: A New Approach to Multiclass Classification and Ranking LT13: Guy Lebanon and John Lafferty: Conditional Models on the Ranking Poset LT14: John Langford and John Shawe-Taylor: PAC-Bayes And Margins VS14: Nuno Vasconcelos: Feature Selection by Maximum Marginal Diversity AP09: Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, and Ismail Ari: Adaptive Caching by Refetching LT15: Eric Allender, Sanjeev Arora, Michael Kearns, Christopher Moore, and Alexander Russell: A Note on the Representational Incompatability of Function Approximation and Factored Dynamics CN09: Ralf Schoknecht and Artur Merke: Convergent Combinations of Reinforcement Learning with Linear Function Approximation CN10: Daniela Pucci de Farias, and Benjamin Van Roy: Approximate Linear programming for Average-Cost Dynamic Programming CN11: Theodore J. Perkins and Doina Precup: A Convergent Form of Approximate Policy Iteration CN12: Ronen Brafman and Moshe Tennenholtz: Efficient Learning Equilibrium
12:00- 2:00		--- lunch break --- Session chair: Andrew Ng
2:00- 2:20		AA40: Luis E. Ortiz and Michael Kearns: Nash Propagation for Loopy Graphical Games
2:20- 2:40		AA41: Dan Pelleg and Andrew Moore: Using Tarjan's Red Rule for Fast Dependency Tree Construction
2:40- 3:00		LT16: Wim Wiegerinck and Tom Heskes: Fractional Belief Propagation
3:00- 3:20		AA42: Martin Wainwright, Tommi Jaakkola, and Alan Willsky: Exact MAP estimates by (hyper)tree agreement
3:20- 3:30		SPOTLIGHTS (5) AA43: Volker Roth, Julian Laub, Joachim M. Buhmann, and Klaus-Robert Muller: Going metric: Denoising pairwise data AA44: Pascal Vincent and Yoshua Bengio: Manifold Parzen Windows AA45: Geoff Hinton and Sam Roweis: Stochastic Neighbor Embedding AA46: Yee Whye Teh and Sam Roweis: Automatic Alignment of Local Representations AA47: David Cohn: Informed Projections
3:30- 3:55		--- coffee break ---
4:00- 4:50		Invited talk: Paul Glimcher (introduced by Eero Simoncelli) Session chair: John Platt
4:50- 5:10		SP06: Hagai Attias: Source Separation with a Microphone Array using Graphical Models and Subband Filtering
5:10- 5:30		AP10: Alexei Vinokourov, John Shawe-Taylor, and Nello Cristianini: Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis
		Thursday
8:30- 9:20		Invited talk: Hugh Durrant-Whyte (introduced by Sebastian Thrun) Session chair: Daniel Lee
9:20- 9:40		LT17: Jon Kleinberg: An Impossibility Theorem for Clustering
9:40-10:00		AA48: Gal Chechik and Naftali Tishby: Extracting relevant structures with side information
10:00-10:50		--- coffee break --- Session chair: Sam Roweis
10:50-11:10		NS07: David Barber: Learning in Spiking Neural Assemblies
11:10-12:00		Invited talk: Andrew Moore (introduced by Marina Meila)
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