ADVANCES IN NEURAL INFORMATION

PROCESSING SYSTEMS 17

Proceedings of the 2004 Conference

edited by

Lawrence K. Saul, Yair Weiss and Léon Bottou

A Bradford BookThe MIT Press

Cambridge, MassachusettsLondon, England

Contents

Contents ν

Preface xix

Donors xxi

NIPS foundation xxii

Committees xxiii

Reviewers xxiv

Learning first-order Markov models for control, PIETER ABBEEL,

and ANDREW Y. NG, Stanford University 1

A Large Deviation Bound for the Area Under the ROC Curve,SHIVANI AGARWAL, University of Illinois at Urbana-Champaign, THORE

GRAEPEL, RALF HERBRICH, Microsoft Research, and DAN ROTH, Universityof Illinois at Urbana-Champaign 9

Learning Preferences for Multiclass Problems, FABIO AIOLLI,

University of Pisa, and ALESSANDRO SPERDUTI, University of Padova 17

Harmonising Chorales by Probabilistic Inference, MORAY ALLAN,

and CHRISTOPHER K. I. WILLIAMS, University of Edinburgh 25

The Correlated Correspondence Algorithm for UnsupervisedRegistration of Nonrigid Surfaces, DRAGOMIR ANGUELOV, PRAVEEN

SRINIVASAN, HOI-CHEUNG PANG, DAPHNE KOLLER, SEBASTIAN THRUN,

Stanford University, and JAMES DAVIS, Honda Research Institute 33

A Direct Formulation for Sparse PCA Using SemidefiniteProgramming, ALEXANDRE D'ASPREMONT, LAURENT EL GHAOUI,

MICHAEL I. JORDAN, and GERT R.G. LANCKRIET, UC Berkeley 41

Comparing Beliefs, Surveys, and Random Walks, ERIK AURELL,

Swedish Institute of Computer Science and Royal Institute of Technology,Stockholm, URI GORDON, Hebrew University, Jerusalem, and SCOTTKlRKPATRICK, Hebrew University, Jerusalem 49

The power of feature clustering: An application to objectdetection, SHAI AVIDAN, Mitsubishi Electric Research Labs, and MOSHEBUTMAN, Adyoron Intelligent Systems 57

Blind One-microphone Speech Separation: A Spectral LearningApproach, FRANCIS R. BACH, and MICHAEL I. JORDAN, UC Berkeley 65

Computing regularization paths for learning multiple kernels,FRANCIS R. BACH, ROMAIN THIBAUX, and MICHAEL I. JORDAN, UC

Berkeley 73

Breaking SVM Complexity with Cross-Training, GÖKHAN H.BAKIR, Max Planck Institute for Biological Cybernetics, LÉON BOTTOU, andJASON WESTON, NEC Labs America 81

Co-Training and Expansion: Towards Bridging Theory andPractice, MARIA-FLORINA BALCAN, AVRIM BLUM, and K E YANG,Carnegie Mellon University 89

Large-Scale Prediction of Disulphide Bond Connectivity, PIERREBALDI, JiANLIN CHENG, UC Irvine, and ALESSANDRO VULLO, University ofFlorence 97

Spike Sorting: Bayesian Clustering of Non-Stationary Data,AHARON BAR-HILLEL, ADAM SPIRO, and ERAN STARK, The HebrewUniversity 105

Exponentiated Gradient Algorithms for Large-margin StructuredClassification, PETER L. BARTLETT, U.C. Berkeley, MICHAEL COLLINS,MIT, BEN TASKAR, Stanford University, and DAVID MCALLESTER, ToyotaTechnological Institute at Chicago 113

Maximising Sensitivity in a Spiking Network, ANTHONY J. BELL,Redwood Neuroscience Institute, and LUCAS C. PARRA, City College of NewYork 121

Non-Local Manifold Tangent Learning, YoSHUA BENGIO, andMARTIN MONPERRUS, Université de Montréal 129

Who's In the Picture, TAMARA L. BERG, ALEXANDER C. BERG,JAETY EDWARDS, and DAVID FORSYTH, UC Berkeley 137

At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks, NILSBERTSCHINGER, MPI MIS Leipzig, THOMAS NATSCHLÄGER, SoftwareCompetence Center Hagenberg, and ROBERT A. LEGENSTEIN, TechnischeUniversität Graz 145

A Second Order Cone programming Formulation for ClassifyingMissing Data, CHIRANJIB BHATTACHARYYA, PANNAGADATTA K.SHIVASWAMY, Indian Institute of Science, and ALEX SMOLA, National ICTAustralia and ANU 153

Support Vector Classification with Input Data Uncertainty, JINBOBl, Siemens Medical Solutions, Inc., and TONG ZHANG, IBM Watson ResearchCenter 161

Responding to Modalities with Different Latencies, FREDRIKBlSSMARCK, ATR International, HlROYUKI NAKAHARA, RIKEN Brain ScienceInstitute, KENJI DOYA, Okinawa Institute of Science and Technology, andOKIHIDE HiKOSAKA, National Eye Institute 169

Nonlinear Blind Source Separation by Integrating IndependentComponent Analysis and Slow Feature Analysis, TOBIAS BLASCHKE,and LAURENZ WISKOTT, Humboldt-Universität zu Berlin 177

Hierarchical Distributed Representations for Statistical LanguageModeling, JOHN BLITZER, KILIAN WEINBERGER, LAWRENCE SAUL, andFERNANDO PEREIRA, University of Pennsylvania 185

Markov Networks for Detecting Overalpping Elements inSequence Data, JOSPEH BOCKHORST, and MARK CRAVEN, University ofWisconsin 193

Reducing Spike Train Variability: A Computational Theory OfSpike-Timing Dependent Plasticity, SANDER M. BOHTE, CWI, andMICHAEL C. MOZER, University of Colorado at Boulder 201

Convergence and No-Regret in Multiagent Learning, MICHAEL

BOWLING, University of Alberta 209

Dependent Gaussian Processes, PHILLIP BOYLE, and MARCUS FREAN,

Victoria University of Wellington 217

Proximity Graphs for Clustering and Manifold Learning, MIGUEL

Á. CARREIRA-PERPINÄN, and RICHARD S. ZEMEL, University of Toronto 225

Incremental Algorithms for Hierarchical Classification, NicoLÖCESA-BIANCHI, Universita degli studi di Milano, CLAUDIO GENTILE,Universita degli studi dell'Insubria, ANDREA TlRONI, and LUCA ZANIBONI,

Universita degli studi di Milano 233

Worst-Case Analysis of Selective Sampling for Linear-ThresholdAlgorithms, NlCOLO CESA-BIANCHI, Università degli Studi di Milano,CLAUDIO GENTILE, Università degli Studi dell'Insubria, and LUCA ZANIBONI,

Università degli Studi di Milano 241

Sub-Microwatt Analog VLSI Support Vector Machine for PatternClassification and Sequence Estimation, SHANTANU CHAKRABARTTY,

Michigan State University, and GERT CAUWENBERGHS, The Johns HopkinsUniversity 249

A Machine Learning Approach to Conjoint Analysis, OLIVIER

CHAPELLE, and ZAID HARCHAOUI, Max Planck Institute 257

Using Machine Learning to Break Visual Human InteractionProofs (HIPs) , KUMAR CHELLAPILLA, and PATRICE Y. SIMARD,

Microsoft Research 265

Hierarchical Eigensolver for Transition Matrices in SpectralMethods , CHAKRA CHENNUBHOTLA, and ALLAN JEPSON, University ofToronto 273

Modeling Conversational Dynamics as a Mixed-Memory MarkovProcess, TANZEEM CHOUDHURY, Intel Research Seattle, and SUMIT BASU,Microsoft Research 281

Theories of Access Consciousness, MICHAEL D. COLAGROSSO,

Colorado School of Mines, and MICHAEL C. MOZER, University of Colorado 289

Distributed Information Regularization on Graphs, ADRIAN

CORDUNEANU, and TOMMI S. JAAKKOLA, Massachusetts Institute ofTechnology · 297

Confidence Intervals for the Area Under the ROC Curve, CORINNA

CORTES, Google Labs, and MEHRYAR M O H R I , New York University 305

Similarity and Discrimination in Classical Conditioning: A LatentVariable Account, AARON C. COURVILLE, Carnegie Mellon University,NATHANIEL Ό. DAW, Gatsby Computational Neuroscience Unit, and DAVID S.TOURETZKY, Carnegie Mellon University 313

Trait Selection for Assessing Beef Meat Quality Using Non-linearSVM, JUAN JOSÉ DEL COZ, GUSTAVO F. BAYÓN, JORGE DIEZ, OSCARLUACES, ANTONIO BAHAMONDE, University of Oviedo, Spain, and CARLOSSANUDO, University of Zaragoza, Spain 321

Semigroup Kernels on Finite Sets, MARCO CUTURI, andJEAN-PHILIPPE VERT, Ecole des Mines de Paris 329

Analysis of a greedy active learning strategy, SANJOY DASGUPTA,UC San Diego 337

The Power of Selective Memory: Self-Bounded Learning ofPrediction Suffix Trees, OFER DEKEL, SHAI SHALEV-SHWARTZ, TheHebrew University, and YORAM SINGER, Google Inc 345

Bayesian inference in spiking neurons, SOPHIE DENEVE, GatsbyComputational Neuroscience Unit - 353

Triangle Fixing Algorithms for the Metric Nearness Problem,INDERJIT DHILLON, SUVRIT SRA, The University of Texas at Austin, andJOEL TROPP, University of Michigan 361

Pictorial Structures for Molecular Modeling: Interpreting DensityMaps, FRANK DIMAIO, JUDE SHAVLIK, and GEORGE PHILLIPS, Universityof Wisconsin - Madison 369

Sparse Coding of Natural Images Using an Overcomplete Set ofLimited Capacity Units, EIZABURO Doi, and MICHAEL S. LEWICKI,Carnegie Mellon University 377

Making Latin Manuscripts Searchable using gHMM's , JAETYEDWARDS, YEE WHYE TEH, DAVID FORSYTH, ROGER BOCK, MICHAELMAIRE, and GRACE VESOM, U.C. Berkeley 385

Seeing through water, ALEXEI EFROS, University of Oxford, VOLKANISLER, JlANBO SHI, and MIRKO VlSONTAI, University of Pennsylvania 393

Experts in a Markov Decision Process, EYAL EVEN-DAR, Tel-AvivUniversity, SHAM M. KAKADE, University of Pennsylvania, and YlSHAYMANSOUR, Tel-Aviv University 401

Exploration-Exploitation Tradeoffs for Experts Algorithms inReactive Environments, DANIELA DE FARIAS, MIT, and NIMRODMEGIDDO, IBM Almaden Research Center 409

A Cost-Shaping LP for Bellman Error Minimization withPerformance Guarantees, DANIELA DE FARIAS, MIT, and BENJAMINVAN ROY, Stanford University 417

Learning Hyper-Features for Visual Identification, ANDRASD. FERENCZ, UC Berkeley, ERIK G. LEARNED-MILLER, University ofMassachusetts Amherst, and JlTENDRA MALIK, UC Berkeley 425

Sampling Methods for Unsupervised Learning, ROB FERGUS,ANDREW ZISSERMAN, University of Oxford, and PlETRO PERONA, CaliforniaInstitute of Technology 433

On-Chip Compensation of Device-Mismatch Effects in AnalogVLSI Neural Networks, MIGUEL FIGUEROA, Universidad de Concepcion,Chile, SETH BRIDGES, and CHRIS DIORIO, University of Washington 441

Object Classification from a Single Example Utilizing ClassRelevance Metrics, MICHAEL FINK, The Hebrew University 449

A Hidden Markov Model for de Novo Peptide Sequencing, BERNDFISCHER, VOLKER ROTH, JOACHIM BUHMANN, JONAS GROSSMANN,

SACHA BAGINSKY, WILHELM GRUISSEM, FRANZ ROOS, and P E T E R

WlDMAYER, ETH Zurich 457

Implicit Wiener Series for Higher-Order Image Analysis,MATTHIAS O. FRANZ, and BERNHARD SCHÖLKOPF, Max-Planck-Institutefor Biological Cybernetics, Tübingen 465

Joint Probabilistic Curve Clustering and Alignment, SCOTT J.GAFFNEY, and PADHRAIC SMYTH, University of California, Irvine 473

Discriminant Saliency for Visual Recognition from ClutteredScenes, DASHAN GAO, and NUNO VASCONCELOS, UC San Diego 481

Instance-Based Relevance Feedback for Image Retrieval, GIORGIO

GlACINTO, and FABIO ROLI, University of Cagliari, Italy 489

Euclidean Embedding of Co-Occurrence Data, AMIR GLOBERSON,

The Hebrew University, GAL CHECHIK, Stanford University, FERNANDOPEREIRA, University of Pennsylvania, and NAFTALI TISHBY, The HebrewUniversity 497

Hierarchical Clustering of a Mixture Model, JACOB GOLDBERGER,and SAM ROWEIS, University of Toronto 505

Neighbourhood Components Analysis, JACOB GOLDBERGER, SAM

ROWEIS, GEOFFREY HINTON, and RUSLAN SALAKHUTDINOV, University ofToronto 513

Parallel Support Vector Machines: The Cascade SVM, HANSP E T E R G R A F , E R I C COSATTO, LÉON BOTTOU, IGOR DOURDANOVIC, and

VLADIMIR VAPNIK, NEC Laboratories America 521

Semi-supervised Learning by Entropy Minimization, YVESGRANDVALET, Heudiasyc, UTC, and YOSHUA BENGIO, Universite de Montreal . 529

Integrating Topics and Syntax, THOMAS L . GRIFFITHS, MassachusettsInstitute of Technology, MARK STEYVERS, University of California, Irvine,DAVID M. BLEI, University of California, Berkeley, and JOSHUA B.TENENBAUM, Massachusetts Institute of Technology 537

Result Analysis of the NIPS 2003 Feature Selection Challenge,ISABELLE GUYON, Clopinet, Berkeley, STEVE GUNN, University ofSouthampton, ASA B E N - H U R , University of Washington, and GIDEON DROR,

Academic College of Tel-Aviv-Yaffo 545

Theory of localized synfire chain: characteristic propagationspeed of stable spike pattern, KosuKE HAMAGUCHI, R I K E N , MASATO

OKADA, RIKEN and Univ. of Tokyo, and KAZUYUKI AIHARA, Univ. of Tokyoand Aihara Complexity Modeling Project ERATO 553

The Entire Regularization Path for the Support Vector Machine,TREVOR HASTIE, Stanford University, SAHARON ROSSET, IBM WatsonResearch Center, ROBERT TlBSHIRANI, Stanford University, and Ji ZHU,University of Michigan 561

An Auditory Paradigm for Brain-Computer Interfaces, Ν. JEREMY

HILL, T H O M A S NAVIN LAL, KARIN B I E R I G , MPI for Biological Cybernetics,

NIELS BIRBAUMER, University of Tübingen, and BERNHARD SCHÖLKOPF,MPI for Biological Cybernetics 569

The Cerebellum Chip: an Analog VLSI Implementation ofa Cerebellar Model of Classical Conditioning, CONSTANZE

HOFSTOETTER, MANUEL GlL, K Y N A N ENG, G I A C O M O INDIVERI,

University/ΕΤΗ Zurich, MATTI MlNTZ, Tel-Aviv University, JOERG KRAMER,and PAUL VERSCHURE, University/ΕΤΗ Zurich 577

Schema Learning: Experience-Based Construction of PredictiveAction Models, M I C H A E L P . H O L M E S , and C H A R L E S L E E ISBELL, J R . ,

Georgia Tech 585

Unsupervised Variational Bayesian Learning of Nonlinear Models,A N T T I H O N K E L A , and HARRI VALPOLA, Helsinki University of Technology 593

A Generalized Bradley-Terry Model: From Group Competitiont o Individual Skill, T z u - K u o HUANG, C H I H - J E N L I N , National TaiwanUniversity, and RUBY C. W E N G , National Chengchi University 601

Message Errors in Belief Propagation, ALEXANDER T. IHLER, JOHN

W . FISHER, and ALAN S. W I L L S K Y , Massachusetts Institute of Technology 609

Parametric Embedding for Class Visualization, ToMOHARU IWATA,K A Z U M I S A I T O , N A O N O R I U E D A , NTT Corporation, SEAN S T R O M S T E N ,

Alphatech, Inc., THOMAS L. GRIFFITHS, and JOSHUA B. T E N E N B A U M ,Massachusetts Institute of Technology 617

The Laplacian PDF Distance: A Cost Function for Clustering ina Kernel Feature Space, ROBERT J E N S S E N , University of Troms0, Norway,D E N I Z E R D O G M U S , Oregon Graduate Institute, OHSU, JOSE PRINCIPE,University of Florida, and TORBJ0RN E L T O F T , University of Troms0, Norway .. . 625

Economic Properties of Social Networks, SHAM M. KAKADE,M I C H A E L K E A R N S , L U I S E. O R T I Z , R O B I N P E M A N T L E , and SIDDHARTH

SURI, University of Pennsylvania 633

Online Bounds for Bayesian Algorithms, SHAM M. KAKADE,University of Pennsylvania, and ANDREW Y. NG, Stanford 641

Maximal Margin Labeling for Multi-Topic Text Categorization,HlDETO KAZAWA, T O M O N O R I IZUMITANI, H I R O T O S H I TAIRA, and ElSAKU

M A E D A , NTT 649

Generalization Error and Algorithmic Convergence of MedianBoost ing, BALAZS K É G L , University of Montreal 657

Boosting on Manifolds: Adaptive Regularization of BaseClassifiers, BALAZS KÉGL, and LIGEN WANG, University of Montreal 665

Face Detection — Efficient and Rank Deficient, WOLF KIENZLE,

GÖKHAN H. BAKIR, MATTHIAS O. F R A N Z , and BERNHARD SCHÖLKOPF,

Max-Planck-Institute for Biological Cybernetics, Tübingen 673

Neural Network Computation by In Vitro TranscriptionalCircuits, JONGMIN KlM, Caltech, JOHN HOPFIELD, Princeton University,and ERIK W I N F R E E , Caltech 681

Synchronization of neural networks by mutual learning and itsapplication to cryptography, E INAT K L E I N , RACHEL MISLOVATY, I D O

KANTER, Bar-ilan University, ANDREAS RUTTOR, and WOLFGANG KlNZEL,Wurzburg University . 689

Nearly Tight Bounds for the Continuum-Armed Bandit Problem,ROBERT Ό. KLEINBERG, MIT 697

Optimal Aggregation of Classifiers and Boosting Maps inFunctional Magnetic Resonance Imaging, VLADIMIR KOLTCHINSKII,

M A N E L MARTINEZ-RAMON, and STEFAN POSSE, University of New Mexico . . . 705

Newscast EM, W O J T E K KOWALCZYK, Vrije Universiteit Amsterdam, andNlKOS VLASSIS, University of Amsterdam 713

On Semi-Supervised Classification, BALAJI KRISHNAPURAM,

Siemens Medical Solutions, USA, DAVID WILLIAMS, YA XUE, ALEXANDERHARTEMINK, LAWRENCE CARIN, Duke University, and MARIO FIGUEIREDO,I.S.T., Technical University of Lisbon, Portugal 721

An Application of Boosting to Graph Classification, TAKU KUDO,

ElSAKU MAEDA, NTT Communication Science Laboratories, NTT Corp., andYUJI MATSUMOTO, Nara Institute of Science and Technology 729

Methods Towards Invasive Human Brain Computer Interfaces,THOMAS NAVIN LAL, Max Planck Institute for Biological Cybernetics, THILOHlNTERBERGER, Eberhard-Karls-University Tuebingen, GUIDO WlDMAN,University of Bonn, MICHAEL SCHRÖDER, Eberhard-Karls-UniversityTuebingen, Ν. JEREMY HlLL, Max Planck Institute for Biological Cybernetics,WOLFGANG ROSENSTIEL, Eberhard Karls University Tuebingen, CHRISTIANE. E L G E R , University of Bonn, BERNHARD SCHÖLKOPF, Max Planck Institutefor Biological Cybernetics, and NIELS BlRBAUMER, Eberhard-Karls-UniversityTuebingen 737

Beat Tracking the Graphical Model Way, DUSTIN LANG, and NANDO

DE FREITAS, University of British Columbia 745

Semi-supervised Learning via Gaussian Processes, NEIL D.LAWRENCE, University of Sheffield, and MICHAEL I. JORDAN, University ofCalifornia, Berkeley · 753

Joint MRI Bias Removal Using Entropy Minimization AcrossImages, ERIK G. LEARNED-MILLER, UMass Amherst, and PARVEZ

AHAMMAD, UC Berkeley 761

Rate- and Phase-coded Autoassociative Memory, MATE LENGYEL,

and PETER DAYAN, Gatsby Computational Neuroscience Unit 769

Maximum Likelihood Estimation of Intrinsic Dimension,ELIZAVETA LEVINA, University of Michigan, and PETER J. BICKEL, Universityof California, Berkeley 777

Planning for Markov Decision Processes with Sparse Stochasticity,MAXIM LIKHACHEV, GEOFF GORDON, Carnegie Mellon University, andSEBASTIAN THRUN, Stanford University 785

Incremental Learning for Visual Tracking, JONGWOO LIM, Universityof Illinois at Urbana Champaign, DAVID A. ROSS, University of Toronto,RUEI-SUNG LlN, University of Illinois at Urbana Champaign, and MlNG-HsUANYANG, Honda Research Institute 793

Adaptive Discriminative Generative Model and Its Applications,RUEI-SUNG LlN, University of Illinois at Urbana Champaign, DAVID A.ROSS, University of Toronto, JONGWOO LlM, University of Illinois at UrbanaChampaign, and MlNG-HSUAN YANG, Honda Research Institute 801

Bayesian Regularization and Nonnegative Deconvolution for TimeDelay Estimation, YuANQiNG LIN, and DANIEL D. L E E , University ofPennsylvania 809

Multiple Alignment of Continuous Time Series, JENNIFER

LISTGARTEN, RADFORD M. NEAL, SAM T. ROWEIS, and ANDREW EMILI,

University of Toronto 817

An Investigation of Practical Approximate Nearest NeighborAlgorithms, TING LIU, ANDREW W . MOORE, ALEXANDER GRAY, andK E YANG, Carnegie Mellon University 825

Mistake Bounds for Maximum Entropy Discrimination, PHILIP M.LONG, Columbia University, and XlNYU Wu, National University of Singapore . . 833

A Three Tiered Approach for Articulated Object Action Modelingand Recognition, LE LU, GREGORY D. HAGER, and LAURENT YOUNES,

Johns Hopkins University 841

Semi-supervised Learning with Penalized Probabilistic Clustering,ZHENGDONG LU, and TODD LEEN, Oregon Health & Science University 849

Limits of Spectral Clustering, ULRIKE VON LUXBURG, OLIVIER

BOUSQUET, Max Planck Institute for Biological Cybernetics, and MlKHAILBELKIN, The University of Chicago 857

Methods for Estimating the Computational Power andGeneralization Capability of Neural Microcircuits, WOLFGANG

MAASS, ROBERT Α. LEGENSTEIN, Technische Universität Graz, and NILSBERTSCHINGER, MPI MIS Leipzig 865

Co-Validation: Using Model Disagreement on Unlabeled Datato Validate Classification Algorithms, OMID MADANI, DAVID M.PENNOCK, and GARY W. FLAKE, Yahoo! Research Labs 873

PAC-Bayes Learning of Conjunctions and Classification ofGene-Expression Data, MARIO MARCHAND, Université Laval, andMOHAK SHAH, University of Ottawa 881

Joint Tracking of Pose, Expression, and Texture usingConditionally Gaussian Filters, TIM Κ. MARKS, University of CaliforniaSan Diego, JOHN HERSHEY, Microsoft Research, J. COOPER RODDEY, andJAVIER Κ. MOVELLAN, University of California San Diego 889

Linear Multilayer Independent Component Analysis for LargeNatural Scenes, YOSHITATSU MATSUDA, and KAZUNORI YAMAGUCHI,The University of Tokyo 897

Conditional Models of Identity Uncertainty with Application toNoun Coreference, ANDREW MCCALLUM, University of MassachusettsAmherst, and BEN WELLNER, The MITRE Corporation 905

Multiple Relational Embedding, ROLAND MEMISEVIC, and GEOFFREYHlNTON, University of Toronto 913

Kernels for Multi-task Learning, CHARLES Α. MICCHELLI, StateUniversity of New York, and MASSIMILIANO PONTIL, University College London . 921

A Topographic Support Vector Machine: ClassificationUsing Local Label Configurations, JOHANNES MOHR, and KLAUSOBERMAYER, Berlin University of Technology 929

Optimal Information Decoding from Neuronal Populations withSpecific Stimulus Selectivity, MARCELO Α. MONTEMURRO, andSTEFANO PANZERI, UMIST 9 3 7

Validity Estimates for Loopy Belief Propagation on BinaryReal-world Networks, JORIS M. Mooij, and HILBERT J. K A P P E N ,Radboud University Nijmegen 945

C o m m o n - F r a m e Model for Object Recognition, P I E R R E M O R E E L S ,and PlETRO PERONA, California Institute of Technology 953

Optimal sub-graphical models, MuKUND NARASIMHAN, and J E F F Α.BlLMES, University of Washington 961

Detecting Significant Multidimensional Spatial Clusters, DANIELB. NEILL, A N D R E W W . M O O R E , FRANCISCO PEREIRA, and T O M

MITCHELL, Carnegie Mellon University 969

Stable adaptive control with online learning, ANDREW Y. NG,Stanford University, and H. JlN KlM, Seoul National University 977

Mass Meta-analysis in Talairach Space, FINN ARUP NIELSEN,Rigshospitalet, Copenhagen 985

A Harmonic Excitation State-Space Approach to Blind Separationof Speech, RASMUS KONGSGAARD OLSSON, and LARS K A I HANSEN,

Technical University of Denmark 993

Expectation Consistent Free Energies for Approximate Inference,MANFRED OPPER, University of Southampton, and O L E W I N T H E R , TechnicalUniversity of Denmark 1001

Discrete profile alignment via constrained information bottleneck,SEAN O ' R O U R K E , Uc San Diego, GAL CHECHIK, Stanford University, ROBINF R I E D M A N , and ELEAZAR ESKIN, uc San Diego 1009

Synergistic Face Detection and Pose Estimation with Energy-Based Models, MARGARITA OSADCHY, Technion, M A T T H E W L . MILLER,

NEC Labs, and YANN L E C U N , New York University 1017

Log-concavity Results on Gaussian Process Methods forSupervised and Unsupervised Learning, LiAM PANINSKI, GatsbyComputational Neuroscience Unit 1025

Variational Minimax Estimation of Discrete Distributions underKL Loss LiAM PANINSKI, Gatsby Computational Neuroscience Unit 1033

Modeling Nonlinear Dependencies in Natural Images usingMixture of Laplacian Distribution, HYUN JIN PARK, and T E WONLEE, Institute for Neural Computation, UCSD 1041

Approximately Efficient Online Mechanism Design, DAVID CPARKES, Harvard University, SATINDER SINGH, University of Michigan, AnnArbor, and DlMAH YANOVSKY, Harvard College 1049

Efficient Out-of-Sample Extension of Dominant-Set Clusters,MASSIMILIANO PAVAN, and MARCELLO PELILLO, University of Venice 1057

A Feature Selection Algorithm Based on the Global Minimizationof a Generalization Error Bound, DORi PELEG, and RON MEIR,Technion - Israel Institute of Technology 1065

Active Learning for Anomaly and Rare-Category Detection, DANP E L L E G , and ANDREW W. MOORE, Carnegie-Mellon University 1073

VDCBPI: an Approximate Scalable Algorithm for LargeP O M D P s , PASCAL POUPART, and CRAIG BOUTILIER, University of Toronto 1081

New Criteria and a New Algorithm for Learning in Multi-AgentSystems, ROB POWERS, and YOAV SHOHAM, Stanford University 1089

Conditional Random Fields for Object Recognition, ARIADNAQuATTONi, M I C H A E L COLLINS, and T R E V O R DARRELL, MIT 1097

Chemosensory Processing in a Spiking Model of the OlfactoryBulb: Chemotopic Convergence and Center Surround Inhibition,BARANIDHARAN RAMAN, and R I C A R D O G U T I E R R E Z - O S U N A , Texas A&M

University 1105

Hierarchical Bayesian Inference in Networks of Spiking Neurons,R A J E S H Ρ. Ν. RAO, University of Washington 1113

An Information Maximization Model of Eye Movements, LAURAW A L K E R R E N N I N G E R , J A M E S M. COUGHLAN, P R E E T I V E R G H E S E ,

Smith-Kettlewell Eye Research Institute, and JlTENDRA MALIK, U.C. Berkeley . 1121

Brain Inspired Reinforcement Learning, FRANÇOIS RIVEST, YOSHUABENGIO, and JOHN ΚALASKA, Université de Montréal 1129

Coarticulation in Markov Decision Processes, KHASHAYAR

ROHANIMANESH, ROBERT PLATT, SRIDHAR MAHADEVAN, and RODERICGRUPEN, University of Massachusetts 1137

Learning, Regularization and Ill-Posed Inverse Problems,LORENZO ROSASCO, DISI, Universita di Genova, ANDREA C A P O N N E T T O ,

DISI Universita di Genova, ERNESTO D E VITO, Dipartimento di Matematica,Universita di Modena, FRANCESCA O D O N E , and UMBERTO D E GIOVANNINI,

DISI Universita di Genova 1145

Following Curved Regularized Optimization Solution Paths,SAHARON ROSSET, IBM T.J. Watson Research Center 1153

A Method for Inferring Label Sampling Mechanisms inSemi-Supervised Learning, SAHARON ROSSET, IBM T.J. WatsonResearch Center, JI ZHU, University of Michigan, HUI ZOU, and TREVORHASTIE, Stanford University 1161

Outlier Detection with One-class Kernel Fisher Discriminants,VOLKER R O T H , ETH Zurich 1169

Semi-parametric Exponential Family PCA, SAJAMA, and ALON

ORLITSKY, Uc San Diego 1177

Semi-Markov Conditional Random Fields for InformationExtraction, SUNITA SARAWAGI, HT Bombay, and WILLIAM W. COHEN,Carnegie Mellon University 1185

Kernel Methods for Implicit Surface Modeling, BERNHARD

SCHÖLKOPF, Max Planck Institute for Biological Cybernetics, JOACHIMGlESEN, and SlMON SPALINGER, ETH Zürich 1193

Edge of Chaos Computation in Mixed-Mode VLSI - A HardLiquid, FELIX SCHÜRMANN, KARLHEINZ M E I E R , and JOHANNES

SCHEMMEL, University of Heidelberg 1201

Learning Gaussian Process Kernels via Hierarchical Bayes, ANTON

SCHWAIGHOFER, Fraunhofer FIRST.IDA, VOLKER T R E S P , and KAI Yu,Siemens Corporate Technology 1209

Assignment of Multiplicative Mixtures in Natural Images, ODELIA

SCHWARTZ, TERRENCE J. SEJNOWSKI, Salk Institute and HHMI, and PETERDAYAN, Gatsby Computational Neuroscience Unit 1217

On the Adaptive Properties of Decision Trees, CLAYTON SCOTT,

Rice University, and ROBERT NOWAK, University of Wisconsin-Madison 1225

Real-Time Pitch Determination of One or More Voices byNonnegative Matrix Factorization, FEI SHA, and LAWRENCE SAUL,

University of Pennsylvania 1233

Probabilistic Inference of Alternative Splicing Events inMicroarray Data, OFER SHAI, BRENDAN J. FREY, QUAID D. MORRIS,

QUN PAN, CHRISTINE MISQUITTA, and BENJAMIN J. BLENCOWE, Universityof Toronto 1241

Resolving Perceptual Aliasing In The Presence Of Noisy Sensors,GUY SHANI, and RONEN I. BRAFMAN, Ben-Gurion University 1249

Algebraic Set Kernels with Application to Inference Over LocalImage Representations, AMNON SHASHUA, and TAMIR HAZAN, HebrewUniversity of Jerusalem 1257

Dynamic Bayesian Networks for Brain-Computer Interfaces,P R A D E E P SHENOY, and RAJESH P . N . RAO, University of Washington 1265

A Temporal Kernel-Based Model for Tracking Hand Movementsfrom Neural Activities, LAVI SHPIGELMAN, KOBY CRAMMER, RONY

PAZ, EILON VAADIA, and YORAM SINGER, Hebrew University 1273

Intrinsically Motivated Reinforcement Learning, SATINDER SINGH,

University of Michigan, ANDREW G. BARTO, University of Massachusetts,Amherst, and NUTTAPONG CHENTANEZ, University of Michigan 1281

Learning Efficient Auditory Codes Using Spikes Predicts CochlearFilters, EVAN C. SMITH, and MICHAEL S. LEWICKI, Carnegie MellonUniversity 1289

Learning Syntactic Patterns for Automatic Hypernym Discovery,RiON SNOW, DANIEL JURAFSKY, and ANDREW Y. NG, Stanford University . 1297

Surface Reconstruction using Learned Shape Models, JAN ERIKSOLEM, Malmo University, Sweden, and FREDRIK KAHL, RSISE, AustralianNational University 1305

Using the Equivalent Kernel to Understand Gaussian ProcessRegression, PETER SOLLICH, King's College London, and CHRISTOPHER K.I. WILLIAMS, University of Edinburgh 1313

Generalization Error Bounds for Collaborative Prediction withLow-Rank Matrices, NATHAN SREBRO, University of Toronto, NOGAALON, Tel Aviv University, and TOMMI S. JAAKKOLA, MIT 1321

Maximum-Margin Matrix Factorization, NATHAN SREBRO, Universityof Toronto, JASON D . M . RENNIE, and TOMMI S. JAAKKOLA, MIT 1329

Density Level Detection is Classification, INGO STEINWART, DONHUSH and CLINT SCOVEL, LOS Alamos National Laboratory I337

Fast Rates to Bayes for Kernel Machines, INGO STEINWART, andCLINT SCOVEL, LOS Alamos National Laboratory 1345

Modelling Uncertainty in the Game of Go, DAVID H. STERN,

Cambridge University, T H O R E G R A E P E L , Microsoft Research, and DAVID J.C. MACKAY, Cambridge University 1353

Constraining a Bayesian Model of Human Visual SpeedPerception, ALAN STOCKER, and EERO SIMONCELLI, New York University . 1361

Distributed Occlusion Reasoning for Tracking with NonparametricBelief Propagation, ERIK B. SUDDERTH, Massachusetts Institute ofTechnology, MICHAEL I. MANDEL, Columbia University, WILLIAM T.FREEMAN, and ALAN S. WILLSKY, Massachusetts Institute of Technology 1369

Temporal-Difference Networks, RICHARD S. SUTTON, and BRIAN

TANNER, University of Alberta 1377

Sharing Clusters among Related Groups: Hierarchical DirichletProcesses, Y E E W H Y E T E H , MICHAEL I. JORDAN, uc Berkeley,MATTHEW J. BEAL, SUNY Buffalo, and DAVID M. BLEI, Carnegie MellonUniversity 1385

Heuristics for Ordering Cue Search in Decision Making, PETER M.TODD, and ANJA DIECKMANN, MPI for Human Development 1393

Contextual Models for Object Detection Using Boosted RandomFields, ANTONIO TORRALBA, MIT, K E V I N P . M U R P H Y , University ofBritish Columbia, and WILLIAM T. FREEMAN, MIT 1401

Spike-timing Dependent Plasticity and Mutual InformationMaximization for a Spiking Neuron Model, TARO TOYOIZUMI, TheUniversity of Tokyo, JEAN-PASCAL P F I S T E R , E P F L , Lausanne, KAZUYUKI

AlHARA, The University of Tokyo, and WULFRAM GERSTNER, EPFL, Lausanne 1409

Synergies between Intrinsic and Synaptic Plasticity in IndividualModel Neurons, JOCHEN TRIESCH, UC San Diego 1417

Matrix Exponential Gradient Updates for On-line Learning andBregman Project ion, KOJI TSUDA, Max Planck Institute for BiologicalCybernetics and AIST, GUNNAR RÄTSCH, Max Planck Institute for BiologicalCybernetics and Fraunhofer FIRST, and MANFRED WARMUTH, Uc Santa Cruz 1425

Supervised Graph Inference, J E A N - P H I L I P P E V E R T , Ecole des Mines deParis, and YOSHIHIRO YAMANISHI, Kyoto University 1433

Binet-Cauchy Kernels, S.V.N. VISHWANATHAN, and ALEX SMOLA,

National ICT Australia 1441

Instance-Specific Bayesian Model Averaging for Classification,SHYAM VISWESWARAN, and GREGORY F . COOPER, University of Pittsburgh . 1449

Saliency-Driven Image Acuity Modulation on a ReconfigurableArray of Spiking Silicon Neurons, R. JACOB VOGELSTEIN, UDAYAN

MALLIK, Johns Hopkins University, EUGENIO CULURCIELLO, Yale University,GERT CAUWENBERGHS, and RALPH ETIENNE-CUMMINGS, Johns HopkinsUniversity 1457

Identifying Protein-Protein Interaction Sites on a Genome-WideScale, HAIDONG WANG, Stanford University, E R A N SEGAL, RockefellerUniversity, ASA B E N - H U R , University of Washington, DAPHNE KOLLER, andDOUGLAS L . BRUTLAG, Stanford University 1465

Adaptive Manifold Learning, J I N G W A N G , ZHENYUE ZHANG, ZhejiangUniversity, and HONGYUAN ZHA, Pennsylvania State University . . . . 1473

Exponential Family Harmoniums with an Application toInformation Retrieval, MAX WELLING, MICHAL ROSEN-ZVI, UC Irvine,

and GEOFFREY HiNTON, University of Toronto 1481

Machine Learning Applied to Perception: Decision Images forGender Classification, FELIX A. WICHMANN, ARNULF Β.Α. G R A F ,

Max Planck Institute for Biological Cybernetics, E E R O SIMONCELLI, New YorkUniversity, HEINRICH H. BÜLTHOFF, and BERNHARD SCHÖLKOPF, MaxPlanck Institute for Biological Cybernetics 1489

The Variational Ising Classifier (VIC) Algorithm for CoherentlyContaminated Data, OLIVER M. C. WILLIAMS, University of Cambridge,ANDREW BLAKE, Microsoft Research, and ROBERTO ClPOLLA, University ofCambridge 1497

Generative Affine Localisation and Tracking, JOHN WINN, andANDREW BLAKE, Microsoft Research Cambridge 1505

4-norm Minimization for Basis Selection, DAVID P. WIPF, andBHASKAR D. R A O , UC San Diego 1513

Multi-agent Cooperation in Diverse Population Games, K. Y.MICHAEL WONG, S. W. LIM, and Z. GAO, Hong Kong University of Scienceand Technology 1521

Efficient Kernel Discriminant Analysis via QR Decomposition,TAO XIONG, JIEPING Y E , University of Minnesota, Ql Ll, University ofDelaware, RAVI JANARDAN, and VLADIMIR CHERKASSKY, University ofMinnesota · 1529

Maximum Margin Clustering, LINLI X U , University of Waterloo, JAMESNEUFELD, BRYCE LARSON, and DALE SCHUURMANS, University of Alberta . . 1537

Using Random Forests in the Structured Language Model, PENGXU, and FREDERICK JELINEK, Johns Hopkins University 1545

Solitaire: Man Versus Machine, XIANG YAN, PERSI DIACONIS,

Stanford University, PAAT RUSMEVICHIENTONG, Cornell University, andBENJAMIN VAN ROY, Stanford University 1553

Efficient Kernel Machines Using the Improved Fast GaussTransform, CHANGJIANG YANG, RAMANI DURAISWAMI, and LARRY

DAVIS, University of Maryland 1561

Two-Dimensional Linear Discriminant Analysis, JIEPING YE, RAVI

JANARDAN, University of Minnesota, and Ql LI, University of Delaware 1569

Inference, Attention, and Decision in a Bayesian NeuralArchitecture, ANGELA J. Y U , and PETER DAYAN, Gatsby ComputationalNeuroscience Unit 1577

The Rescorla-Wagner Algorithm and Maximum LikelihoodEstimation of Causal Parameters, ALAN YUILLE, UCLA 1585

The Convergence of Contrastive Divergences, ALAN YUILLE, UCLA . 1593

Self-Tuning Spectral Clustering, Lihi ZELNIK-MANOR, and PIETRO

PERONA, California Institute of Technology 1601

Probabilistic Computation in Spiking Populations, RICHARD S.ZEMEL, University of Toronto, QUENTIN J. M. HUYS, Gatsby Computational

Neuroscience Unit, RAMA NATARAJAN, University of Toronto, and PETERDAYAN, Gatsby Computational Neuroscience Unit 1609

A Probabilistic Model for Online Document Clustering withApplication to Novelty Detection, JlAN ZHANG, Carnegie MellonUniversity, ZOUBIN GHAHRAMANI, Gatsby Computational Neuroscience Unit,and YlMING YANG, Carnegie Mellon University 1617

Class-size Independent Generalization Analsysis of SomeDiscriminative Multi-Category Classification, TONG ZHANG, IBM TJWatson Research Center 1625

Semi-supervised Learning on Directed Graphs, DENGYONG ZHOU,

BERNHARD SCHÖLKOPF, Max Planck Institute for Biological Cybernetics, andTHOMAS HOFMANN, Brown University 1633

Nonparametric Transforms of Graph Kernels for Semi-SupervisedLearning, XlAOJlN ZHU, Carnegie Mellon University, JAZ KANDOLA,

ZOUBIN GHAHRAMANI, Gatsby Computational Neuroscience Unit, and JOHNLAFFERTY, Carnegie Mellon University 1641

Kernel Projection Machine: a New Tool for Pattern Recognition,LAURENT ZWALD, Université Paris-Sud, REGIS VERT, LRI, UniversitéParis-Sud, GILLES BLANCHARD, Fraunhofer First (IDA), and PASCALMASSART, Université Paris-Sud 1649

Subject Index 1657

Author Index 1664