NIPS Workshops 2009 - Whistler

NIPS Workshops, Whistler 2009

The Post-Conference Workshop Program covered a wide range of topics from Neuroscience to Machine Learning. For more workshop schedule information please consult the Workshop URL on the workshop's page.

Detailed information can be found at NIPS 2009 Workshops Homepage.

Click on the picture for the videos from 2009 NIPS Conference in Vancouver.

NIPS Conference 2009 - Vancouver

23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 200923rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver ...

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Computational Biology Clustering Probabilistic Approaches Language Learning Learning from Multiple Sources Optimization Kernels for Multiple Outputs and Multi-task Learning Large Scale Graphical Models Structured Data Machine Learning Temporal Segmentation Generative / Discriminative Interface


Machine Learning in Computational Biology
[syn] 226 views, 23:45 Direct Maximization of Protein Identifications from Tandem Mass SpectraDirect Maximization of Protein Identifications from Tandem Mass Spectra Marina Spivak Marina Spivak	[syn] 218 views, 26:36 Exploiting Physico-Chemical Properties in String-KernelsExploiting Physico-Chemical Properties in String-Kernels Nora Toussaint Nora Toussaint	[syn] 235 views, 24:49 A Bayesian Method for 3D Reconstruction of Macromolecular Structure Using Class Averages from Single Particle Electron MicroscopyA Bayesian Method for 3D Reconstruction of Macromolecular Structure Using ... Navdeep Jaitly Navdeep Jaitly	[syn] 250 views, 21:45 vbFRET: A Bayesian Approach to Single-Molecule Forster Resonance Energy Transfer AnalysisvbFRET: A Bayesian Approach to Single-Molecule Forster Resonance Energy Transfer ... Jonathan Eiseman Bronson Jonathan Eiseman Bronson 1 comment	[syn] 75 views, 24:49 Leveraging Joint Test Status Distribution for an Optimal Significance TestingLeveraging Joint Test Status Distribution for an Optimal Significance Testing Eleazar Eskin Eleazar Eskin
[syn] 303 views, 25:23 Statistical Methods for Ultra-Deep Pyrosequencing of Fast Evolving VirusesStatistical Methods for Ultra-Deep Pyrosequencing of Fast Evolving Viruses David Knowles David Knowles	[syn] 232 views, 23:17 A Machine Learning Pipeline for Phenotype Prediction from Genotype DataA Machine Learning Pipeline for Phenotype Prediction from Genotype Data Cesare Furlanello Cesare Furlanello	[syn] 270 views, 24:35 Association Mapping of Traits over Time Using Gaussian ProcessesAssociation Mapping of Traits over Time Using Gaussian Processes Oliver Stegle Oliver Stegle	[syn] 271 views, 11:48:40 Learning Graphical Model Structure with Sparse Bayesian Factor Models and Process PriorsLearning Graphical Model Structure with Sparse Bayesian Factor Models and ... Ricardo Henao Ricardo Henao	[syn] 140 views, 21:26 Abstraction Augmented Markov ModelsAbstraction Augmented Markov Models Adrian Silvescu Adrian Silvescu
30:46 Discussion: Machine Learning in Computational BiologyDiscussion: Machine Learning in Computational Biology
Clustering: Science or Art? Towards Principled Approaches
[syn] 426 views, 44:36 Introduction: Presentations of Different Views on Clustering by the Workshop OrganizersIntroduction: Presentations of Different Views on Clustering by the Workshop ... Ulrike von Luxburg, Shai Ben-David, Avrim Blum Ulrike von Luxburg, Shai Ben-David, Avrim Blum	[syn] 562 views, 33:21 What Is a Cluster: Perspectives from Game TheoryWhat Is a Cluster: Perspectives from Game Theory Marcello Pelillo Marcello Pelillo	[syn] 178 views, 26:14 Clustering with Prior InformationClustering with Prior Information Greg Ver Steeg Greg Ver Steeg	[syn] 123 views, 27:16 Finding a Better k: A Psychophysical Investigation of ClusteringFinding a Better k: A Psychophysical Investigation of Clustering Joshua M. Lewis Joshua M. Lewis	191 views, 14:37 Single Data, Multiple ClusteringsSingle Data, Multiple Clusterings Sajib Dasgupta Sajib Dasgupta
[syn] 398 views, 10:36 Empricial Study of Cluster Evaluation MetricsEmpricial Study of Cluster Evaluation Metrics Alireza Hadj Khodabakhshi Alireza Hadj Khodabakhshi	[syn] 446 views, 24:42 Invited Talk Clustering Applications at Yahoo!Clustering Applications at Yahoo! Deepayan Chakrabarti Deepayan Chakrabarti	[syn] 161 views, 27:55 Some Ideas for Formalizing ClusteringSome Ideas for Formalizing Clustering Facundo Memoli Facundo Memoli	[syn] 138 views, 21:18 Characterization of Linkage Based ClusteringCharacterization of Linkage Based Clustering David Loker David Loker	[syn] 304 views, 33:57 Information Theoretic Model Selection in ClusteringInformation Theoretic Model Selection in Clustering Joachim M. Buhmann Joachim M. Buhmann
[syn] 76 views, 31:10 PAC-Bayesian Approach to Formulation of Clustering ObjectivesPAC-Bayesian Approach to Formulation of Clustering Objectives Yevgeny Seldin Yevgeny Seldin
Probabilistic Approaches for Robotics and Control
[syn] 173 views, 23:51 Planning under Uncertainty Using Distributions over PosteriorsPlanning under Uncertainty Using Distributions over Posteriors Nicholas Roy Nicholas Roy	[syn] 482 views, 21:09 GP-BayesFilters: Gaussian Process Regression for Bayesian FilteringGP-BayesFilters: Gaussian Process Regression for Bayesian Filtering Dieter Fox Dieter Fox	[syn] 249 views, 19:12 Imitation Learning and Purposeful Prediction: Probabilistic and Non-probabilistic Methods Imitation Learning and Purposeful Prediction: Probabilistic and Non-probabilistic Methods Drew Bagnell Drew Bagnell	[syn] 211 views, 23:42 Probabilistic Control in Human Computer InteractionProbabilistic Control in Human Computer Interaction Roderick Murray-Smith Roderick Murray-Smith	[syn] 110 views, 18:08 Estimating the Sources of Motor ErrorsEstimating the Sources of Motor Errors Konrad Körding Konrad Körding
[syn] 920 views, 23:02 Linear Bellman Equations: Theory and ApplicationsLinear Bellman Equations: Theory and Applications Emanuel Todorov Emanuel Todorov	[syn] 707 views, 20:08 KL Control Theory and Decision Making under UncertaintyKL Control Theory and Decision Making under Uncertainty Bert Kappen Bert Kappen	[syn] 119 views, 29:08 Linear Bellman Combination for Simulation of Human MotionLinear Bellman Combination for Simulation of Human Motion Jovan Popović Jovan Popović	[syn] 26:15 Reinforcement Learning in High Dimensional State Spaces: A Path Integral ApproachReinforcement Learning in High Dimensional State Spaces: A Path Integral ... Evangelos Theodorou Evangelos Theodorou	[syn] 86 views, 24:51 Probabilistic Design: Promises and ProspectsProbabilistic Design: Promises and Prospects Miroslav Kárný Miroslav Kárný
[syn] 274 views, 26:23 Approximate Inference Control Approximate Inference Control Marc Toussaint Marc Toussaint 1 comment
Grammar Induction, Representation of Language and Language Learning
[syn] 240 views, 46:13 Invited Talk Inference for PCFGs and Adaptor GrammarsInference for PCFGs and Adaptor Grammars Mark Johnson Mark Johnson	[syn] 189 views, 20:59 Learning to Disambiguate Natural Language Using World KnowledgeLearning to Disambiguate Natural Language Using World Knowledge Antoine Bordes Antoine Bordes	[syn] 342 views, 16:28 Language Modeling with Tree Substitution GrammarsLanguage Modeling with Tree Substitution Grammars Matt Post Matt Post	[syn] 438 views, 20:18 A Preliminary Evaluation of Word Representations for Named-Entity RecognitionA Preliminary Evaluation of Word Representations for Named-Entity Recognition Joseph Turian Joseph Turian	[syn] 490 views, 54:42 Learnable Representations for Natural LanguageLearnable Representations for Natural Language Alexander Clark Alexander Clark
[syn] 428 views, 57:28 Invited Talk Learning Languages and Rational KernelsLearning Languages and Rational Kernels Mehryar Mohri Mehryar Mohri 1 comment	[syn] 644 views, 25:07 Sparsity in Grammar InductionSparsity in Grammar Induction Jennifer A. Gillenwater Jennifer A. Gillenwater 1 comment	07:14 Poster SpotlightsPoster Spotlights Kewei Tu, Edouard Gilbert, et al. Kewei Tu, Edouard Gilbert, Adam R. Teichert, Mehmet Ali Yatbaz, Valentin I. Spitkovsky, Oskar Kohonen
Learning from Multiple Sources with Applications to Robotics
[syn] 252 views, 59:23 Invited Talk Where's What? - Towards Semantic Mapping of Urban EnvironmentsWhere's What? - Towards Semantic Mapping of Urban Environments Ingmar Posner Ingmar Posner	[syn] 177 views, 20:16 A Bayesian Approach to Occupancy Mapping with Uncertain InputsA Bayesian Approach to Occupancy Mapping with Uncertain Inputs Simon T. O'Callaghan Simon T. O'Callaghan	[syn] 182 views, 18:54 Domain Adaptation for Mobile Robot NavigationDomain Adaptation for Mobile Robot Navigation David Bradley David Bradley	[syn] 331 views, 16:00 Learning CRF Models from Drill Rig Sensors for Autonomous MiningLearning CRF Models from Drill Rig Sensors for Autonomous Mining Sildomar T. Monteiro Sildomar T. Monteiro	[syn] 241 views, 06:59 Poster SpotlightsPoster Spotlights Brenna D. Argall, Bertrand Douillard, et al. Brenna D. Argall, Bertrand Douillard, Amrish S. Kapoor, Jesús Martínez-Gómez, Arman Melkumyan
[syn] 286 views, 56:09 Invited Talk Multi-Task Learning with Gaussian Processes with Applications to Robot Inverse DynamicsMulti-Task Learning with Gaussian Processes with Applications to Robot Inverse ... Chris Williams Chris Williams	[syn] 190 views, 20:06 Multitask Learning Using Nonparametrically Learned Predictor SubspacesMultitask Learning Using Nonparametrically Learned Predictor Subspaces Piyush Rai Piyush Rai	[syn] 155 views, 21:20 Bayesian Localized Multiple Kernel Learning Bayesian Localized Multiple Kernel Learning C. Mario Christoudias C. Mario Christoudias	[syn] 262 views, 15:45 Multi-Way, Multi-View LearningMulti-Way, Multi-View Learning Ilkka Huopaniemi Ilkka Huopaniemi	[syn] 100 views, 22:21 Information Theoretic Kernel IntegrationInformation Theoretic Kernel Integration Yiming Ying Yiming Ying
[syn] 48 views, 28:58 Discussion and Future DirectionsDiscussion and Future Directions Jaakko Peltonen Jaakko Peltonen
Optimization for Machine Learning
[syn] 394 views, 49:35 Invited Talk Chordal Sparsity in Semidefinite Programming and Machine LearningChordal Sparsity in Semidefinite Programming and Machine Learning Lieven Vandenberghe Lieven Vandenberghe	[syn] 316 views, 22:06 A Pathwise Algorithm for Covariance SelectionA Pathwise Algorithm for Covariance Selection Selin Damla Ahipasaoglu Selin Damla Ahipasaoglu	[syn] 300 views, 18:18 Active Set Algorithm for Structured Sparsity-Inducing NormActive Set Algorithm for Structured Sparsity-Inducing Norm Rodolphe Jenatton Rodolphe Jenatton	[syn] 637 views, 53:01 Invited Talk On Recent Trends in Extremely Large-Scale Convex OptimizationOn Recent Trends in Extremely Large-Scale Convex Optimization Arkadi Nemirovski Arkadi Nemirovski	[syn] 176 views, 17:12 Tree Based Ensemble Models Regularization by Convex OptimizationTree Based Ensemble Models Regularization by Convex Optimization Bertrand Cornelusse Bertrand Cornelusse
[syn] 370 views, 20:21 On the Convergence of the Convex-Concave ProcedureOn the Convergence of the Convex-Concave Procedure Bharath K. Sriperumbudur Bharath K. Sriperumbudur	[syn] 260 views, 22:16 SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance MatrixSINCO - An Efficient Greedy Method for Learning Sparse INverse ... Katya Scheinberg Katya Scheinberg	[syn] 537 views, 20:34 Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse LearningSuper-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning Ryota Tomioka Ryota Tomioka
Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View
[syn] 938 views, 48:17 Invited Talk Geostatistics for Gaussian Processes Geostatistics for Gaussian Processes Hans Wackernagel Hans Wackernagel	[syn] 420 views, 1:01:21 Invited Talk Gaussian Processes and Process Convolutions from a Bayesian PerspectiveGaussian Processes and Process Convolutions from a Bayesian Perspective Dave Higdon Dave Higdon	[syn] 349 views, 41:30 Prior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian ProcessesPrior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian ... Mauricio Alvarez Mauricio Alvarez	[syn] 319 views, 37:51 Invited Talk Multi-Task Learning and Matrix RegularizationMulti-Task Learning and Matrix Regularization Andreas Argyriou Andreas Argyriou	[syn] 263 views, 46:34 Invited Talk Learning Vector Fields with Spectral FilteringLearning Vector Fields with Spectral Filtering Lorenzo Rosasco Lorenzo Rosasco
[syn] 187 views, 32:33 Invited Talk Borrowing Strength, Learning Vector Valued Functions and Supervised Dimension ReductionBorrowing Strength, Learning Vector Valued Functions and Supervised Dimension Reduction Sayan Mukherjee Sayan Mukherjee
Approximate Learning of Large Scale Graphical Models: Theory and Applications
[syn] 397 views, 33:05 Approximate Inference in Natural Language ProcessingApproximate Inference in Natural Language Processing Noah Smith Noah Smith	[syn] 1429 views, 34:49 Learning Deep Boltzmann MachinesLearning Deep Boltzmann Machines Ruslan Salakhutdinov Ruslan Salakhutdinov	[syn] 401 views, 41:56 Joint Max Margin and Max Entropy Learning of Graphical ModelsJoint Max Margin and Max Entropy Learning of Graphical Models Eric P. Xing Eric P. Xing	[syn] 515 views, 43:31 Large-Scale Learning and Inference: What We Have Learned with Markov Logic NetworksLarge-Scale Learning and Inference: What We Have Learned with Markov ... Pedro Domingos Pedro Domingos	[syn] 232 views, 17:04 Parameter Learning Using Approximate MAP Inference Parameter Learning Using Approximate MAP Inference Pawan Kumar Mudigonda Pawan Kumar Mudigonda
[syn] 122 views, 28:56 Training Structured Predictors for Novel Loss FunctionsTraining Structured Predictors for Novel Loss Functions David McAllester David McAllester	[syn] 684 views, 30:16 Some Machine Learning Problems that We in the Computer Vision Community Would Like to See SolvedSome Machine Learning Problems that We in the Computer Vision ... William T. Freeman William T. Freeman
Transfer Learning for Structured Data
[syn] 421 views, 42:44 Keynote Speaker Covariate Shift by Kernel Mean MatchingCovariate Shift by Kernel Mean Matching Arthur Gretton Arthur Gretton	[syn] 20:52 A Bayesian Matrix Model for Relational DataA Bayesian Matrix Model for Relational Data Ajit Singh Ajit Singh	[syn] 2 views, 44:44 Keynote Speaker Transfer Learning and Multiple Output Kernel FunctionsTransfer Learning and Multiple Output Kernel Functions Neil D. Lawrence Neil D. Lawrence	[syn] 220 views, 21:21 Kernel Learning and Meta Kernels for Transfer LearningKernel Learning and Meta Kernels for Transfer Learning Ulrich Rückert Ulrich Rückert	[syn] 45:22 Keynote Speaker Transfer Learning Methods and Applications in Computational BiologyTransfer Learning Methods and Applications in Computational Biology Gunnar Rätsch Gunnar Rätsch
[syn] 468 views, 55:03 Keynote Speaker No-Free-Lunch Theorems for Transfer Learning No-Free-Lunch Theorems for Transfer Learning Shai Ben-David Shai Ben-David
Large-Scale Machine Learning: Parallelism and Massive Datasets
201 views, 26:57 Parallel Exact Inference on Multi-Core ProcessorsParallel Exact Inference on Multi-Core Processors Viktor Prasanna Viktor Prasanna	[syn] 594 views, 24:06 Parallel Online LearningParallel Online Learning John Langford John Langford	[syn] 1567 views, 25:08 Probabilistic Machine Learning in Computational AdvertisingProbabilistic Machine Learning in Computational Advertising Thore Graepel Thore Graepel	251 views, 19:30 Parallel Topic Models Parallel Topic Models Alexander J. Smola Alexander J. Smola	[syn] 444 views, 25:08 Scalable Learning in Computer VisionScalable Learning in Computer Vision Adam Coates Adam Coates
1112 views, 20:02 Hadoop-ML: An Infrastructure for the Rapid Implementation of Parallel Reusable AnalyticsHadoop-ML: An Infrastructure for the Rapid Implementation of Parallel Reusable ... Edwin Pednault Edwin Pednault	[syn] 200 views, 56:12 Recreational Activities and DiscussionRecreational Activities and Discussion John Langford John Langford	893 views, 21:43 Large-Scale Machine Learning: The Problems, Algorithms, and ChallengesLarge-Scale Machine Learning: The Problems, Algorithms, and Challenges Alex Gray Alex Gray	166 views, 20:32 1 Billion Instances, 1 Thousand Machines and 3.5 Hours1 Billion Instances, 1 Thousand Machines and 3.5 Hours Daniel Walker Daniel Walker	[syn] 1457 views, 25:00 FPGA-based MapReduce Framework for Machine LearningFPGA-based MapReduce Framework for Machine Learning Ningyi Xu Ningyi Xu
96 views, 24:28 Large-Scale Graph-based Transductive InferenceLarge-Scale Graph-based Transductive Inference Jeff A. Bilmes Jeff A. Bilmes	[syn] 269 views, 13:32 Splash Belief Propagation: Efficient Parallelization Through Asynchronous SchedulingSplash Belief Propagation: Efficient Parallelization Through Asynchronous Scheduling Joseph Gonzalez Joseph Gonzalez
Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing
130 views, 39:39 Retrospective Change-point Approaches and Sequential ModellingRetrospective Change-point Approaches and Sequential Modelling Haipeng Xing Haipeng Xing	361 views, 20:12 A Dynamic HMM for Online SegmentationA Dynamic HMM for Online Segmentation Jens Kohlmorgen Jens Kohlmorgen	83 views, 12:14 Distributed Detection and Localization of Network Anomalies using Rank TestsDistributed Detection and Localization of Network Anomalies using Rank Tests Alexandre Lung-Yut-Fong Alexandre Lung-Yut-Fong	208 views, 25:44 Product Partition Models for Modelling Changing Dependency Structure in Time Series Product Partition Models for Modelling Changing Dependency Structure in Time ... Kevin P. Murphy Kevin P. Murphy	627 views, 34:19 Hierarchical-Dirichlet-Process-based Hidden Markov ModelsHierarchical-Dirichlet-Process-based Hidden Markov Models Erik Sudderth Erik Sudderth
238 views, 41:40 Quickest Change DetectionQuickest Change Detection Olympia Hadjiliadis Olympia Hadjiliadis	20 views, 39:53 Mode Estimation of Autonomous SystemsMode Estimation of Autonomous Systems Brian Williams Brian Williams	[syn] 516 views, 19:58 Adaptive Sequential Bayesian Change-point Detection Adaptive Sequential Bayesian Change-point Detection Ryan Turner Ryan Turner 2 comments	191 views, 38:17 Temporal Segmentation with Kernel Change-point DetectionTemporal Segmentation with Kernel Change-point Detection Francis R. Bach Francis R. Bach
Generative and Discriminative Learning Interface
[syn] 133 views, 19:31 Opening Address for the NIPS WS on the Generative and Discriminative Learning InterfaceOpening Address for the NIPS WS on the Generative and ... Simon Lacoste-Julien, Percy Liang Simon Lacoste-Julien, Percy Liang	[syn] 470 views, 34:02 Invited Talk Generative and Discriminative Models in Statistical ParsingGenerative and Discriminative Models in Statistical Parsing Michael Collins Michael Collins	[syn] 121 views, 18:20 Generative and Discriminative Latent Variable GrammarsGenerative and Discriminative Latent Variable Grammars Slav Petrov Slav Petrov	[syn] 52 views, 19:03 Discriminative and Generative Views of Binary ExperimentsDiscriminative and Generative Views of Binary Experiments Robert C. Williamson Robert C. Williamson	[syn] 264 views, 37:23 Invited Talk Multi-Task Discriminative Estimation for Generative Models and ProbabilitiesMulti-Task Discriminative Estimation for Generative Models and Probabilities Tony Jebara Tony Jebara
[syn] 286 views, 29:32 Invited Talk Generative and Discriminative Image ModelsGenerative and Discriminative Image Models John Winn John Winn	[syn] 440 views, 21:53 Learning Feature Hierarchies by Learning Deep Generative ModelsLearning Feature Hierarchies by Learning Deep Generative Models Ruslan Salakhutdinov Ruslan Salakhutdinov	[syn] 458 views, 22:19 Why Does Unsupervised Pre-training Help Deep Discriminant Learning?Why Does Unsupervised Pre-training Help Deep Discriminant Learning? Dumitru Erhan Dumitru Erhan	[syn] 180 views, 21:32 Unsupervised Learning by Discriminating Data from Artificial NoiseUnsupervised Learning by Discriminating Data from Artificial Noise Michael Gutmann Michael Gutmann

Reviews and comments:

1 Tony Jebara, February 26, 2010 at 9:17 p.m.:

Why are some of the videos for NIPS 2009 workshops locked and unviewable?

Tony Jebara

2 Simon Lacoste-Julien, March 3, 2010 at 12:30 a.m.:

Hi Tony, some videos are locked most probably because they are still being processed.

3 Davor Orlic (staff), March 29, 2010 at 8:57 p.m.:

Tony, Simon, the lectures are locked because some of the speakers didn't want to publish their talks due to new/unpublished scientific material/results.

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NIPS Workshops 2009 - Whistler

NIPS Workshops, Whistler 2009

Event sections

Machine Learning in Computational Biology

Clustering: Science or Art? Towards Principled Approaches

Probabilistic Approaches for Robotics and Control

Grammar Induction, Representation of Language and Language Learning

Learning from Multiple Sources with Applications to Robotics

Optimization for Machine Learning

Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View

Approximate Learning of Large Scale Graphical Models: Theory and Applications

Transfer Learning for Structured Data

Large-Scale Machine Learning: Parallelism and Massive Datasets

Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing

Generative and Discriminative Learning Interface

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