Schedule

Tutorials (December 3, 2007)

The pre-Conference Tutorials were held at the Hyatt Regency Vancouver, British Columbia, Canada on Monday, December 3, 2007.

The Tutorials provided a choice of six two-hour tutorials by leading scientists. The topics spanned a wide range of subjects including Neuroscience, Learning Algorithms and Theory, Bioinformatics, Image Processing, and Data Mining.

Both slides and videos from the Tutorials are available below by clicking the Tutorial title or by clicking the icons to the right of the title. All the Tutorial videos except "Structured Prediction" are in two parts. If you would prefer to download the entire collection of movies and slides for local viewing (1.1 GB), then choose one of the following: tar.gz format or zip format.  

 
Sponsored by Microsoft Research 

Sunday
4:00 - 9:00pm	Registration
Monday
7:30 - 9:00am	Breakfast
8:00am - 6:00pm	Registration
9:30 - 11:30am	Michael S Lewicki Sensory Coding and Hierarchical Representations
9:30 - 11:30am	Robert E Schapire Theory and Applications of Boosting
11:30am - 1:00pm	Lunch
1:00 - 3:00pm	Leon Bottou, Andrew W Moore Learning Using Many Examples
1:00 - 3:00pm	Tomaso A Poggio Visual Recognition in Primates and Machines
3:30 - 5:30pm	Geoffrey E Hinton Deep Belief Nets
3:30 - 5:30pm	Ben Taskar Structured Prediction

Conference Sessions (December 3-6, 2007)

Monday
6:30 - 7:30pm	Opening Banquet
7:30 - 8:00pm	Luis von Ahn Human Computation
8:10 - 8:25pm	SpotlightsSpotlights Czar:Dale Schuurmans
	K. Toutanova, M. Johnson A Bayesian LDA-based model for semi-supervised part-of-speech tagging M. Hoffman, A. Doucet, N. de Freitas, A. Jasra Bayesian Policy Learning with Trans-Dimensional MCMC P. Long, R. Servedio Boosting the Area under the ROC Curve D. Lashkari, P. Golland Convex Clustering with Exemplar-Based Models C. Teo, A. Globerson, S. Roweis, A. Smola Convex Learning with Invariances C. Frogner, A. Pfeffer Discovering Weakly-Interacting Factors in a Complex Stochastic Process S. Petrov, D. Klein Discriminative Log-Linear Grammars with Latent Variables X. Nguyen, M. Wainwright, M. Jordan Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization A. Howard, T. Jebara Learning Monotonic Transformations for Classification C. Walder, O. Chapelle Learning with Transformation Invariant Kernels M. Zinkevich, M. Johanson, M. Bowling, C. Piccione Regret Minimization in Games with Incomplete Information B. Blum, D. Baker, M. Jordan, P. Bradley, R. Das, D. Kim Resampling Methods for Protein Structure Prediction with Rosetta J. Lafferty, L. Wasserman Statistical Analysis of Semi-Supervised Regression A. Kulesza, F. Pereira Structured Learning with Approximate Inference K. Sinha, M. Belkin The Value of Labeled and Unlabeled Examples when the Model is Imperfect
8:30pm - 12:00am	Poster Session
	M. Zinkevich, M. Johanson, M. Bowling, C. Piccione Regret Minimization in Games with Incomplete Information M. Johanson, M. Zinkevich, M. Bowling Computing Robust Counter-Strategies E. Hazan, S. Kale Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria J. Goldsmith, M. Mundhenk Competition Adds Complexity Y. Tassa, T. Erez, B. Smart Receding Horizon Differential Dynamic Programming C. Atkeson, B. Stephens Random Sampling of States in Dynamic Programming M. Hoffman, A. Doucet, N. de Freitas, A. Jasra Bayesian Policy Learning with Trans-Dimensional MCMC S. Ross, J. Pineau, B. Chaib-draa Theoretical Analysis of Heuristic Search Methods for Online POMDPs A. Tewari, P. Bartlett Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs J. Langford, T. Zhang The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information A. Strehl, M. Littman Online Linear Regression and Its Application to Model-Based Reinforcement Learning V. Manfredi, J. Kurose Scan Strategies for Meteorological Radars B. Blum, D. Baker, M. Jordan, P. Bradley, R. Das, D. Kim Resampling Methods for Protein Structure Prediction with Rosetta L. Cayton, S. Dasgupta A learning framework for nearest neighbor search N. Le Roux, P. Manzagol, Y. Bengio Topmoumoute Online Natural Gradient Algorithm M. Holmes, A. Gray, C. Isbell Multi-Stage Monte Carlo Approximation for Fast Generalized Data Summations D. Lashkari, P. Golland Convex Clustering with Exemplar-Based Models C. Hegde, R. Baraniuk Random Projections for Manifold Learning Y. Freund, S. Dasgupta, M. Kabra, N. Verma Learning the structure of manifolds using random projections P. Li, T. Hastie A Unified Near-Optimal Estimator For Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Sta P. Hoff Modeling homophily and stochastic equivalence in symmetric relational data K. Sinha, M. Belkin The Value of Labeled and Unlabeled Examples when the Model is Imperfect Y. Guo, D. Schuurmans Discriminative Batch Mode Active Learning B. Settles, M. Craven, S. Ray Multiple Instance Active Learning E. Brochu, N. de Freitas, A. Ghosh Active Preference Learning with Discrete Choice Data L. Bottou, O. Bousquet The Tradeoffs of Large Scale Learning J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J. Vaughan Learning Bounds for Domain Adaptation M. Mohri, A. Rostamizadeh Stability Bounds for Non-i.i.d. Processes A. Howard, T. Jebara Learning Monotonic Transformations for Classification S. Zhou, J. Lafferty, L. Wasserman Compressed Regression J. Lafferty, L. Wasserman Statistical Analysis of Semi-Supervised Regression P. Long, R. Servedio Boosting the Area under the ROC Curve M. Barreno, A. Cárdenas, D. Tygar Optimal ROC Curve for a Combination of Classifiers Z. Harchaoui, C. Lévy-Leduc Catching Change-points with Lasso D. Wipf, S. Nagarajan A New View of Automatic Relevance Determination R. Luss, A. d'Aspremont Support Vector Machine Classification with Indefinite Kernels F. Richardson, W. Campbell Discriminative Keyword Selection Using Support Vector Machines X. Nguyen, M. Wainwright, M. Jordan Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization C. Walder, O. Chapelle Learning with Transformation Invariant Kernels R. Salakhutdinov, G. Hinton Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes C. Do, C. Foo, A. Ng Efficient multiple hyperparameter learning for log-linear models A. Kulesza, F. Pereira Structured Learning with Approximate Inference Y. Guo, D. Schuurmans Convex Relaxations of EM A. Chechetka, C. Guestrin Efficient Principled Learning of Thin Junction Trees S. Sanghavi, D. Malioutov, A. Willsky Linear programming analysis of loopy belief propagation for weighted matching A. Globerson, T. Jaakkola Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations K. Jung, D. Shah Local Algorithms for Approximate Inference in Minor-Excluded Graphs V. Chandrasekaran, J. Johnson, A. Willsky Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis L. Ortiz CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation J. Platt, E. Kiciman, D. Maltz Fast Variational Inference for Large-scale Internet Diagnosis C. Frogner, A. Pfeffer Discovering Weakly-Interacting Factors in a Complex Stochastic Process V. Raykar, H. Steck, B. Krishnapuram, C. Dehing-Oberije, P. Lambin On Ranking in Survival Analysis: Bounds on the Concordance Index M. Ranzato, Y. Boureau, Y. LeCun Sparse Feature Learning for Deep Belief Networks S. Osindero, G. Hinton Modeling image patches with a directed hierarchy of Markov random fields Z. Lu, M. Carreira-Perpinan, C. Sminchisescu People Tracking with the Laplacian Eigenmaps Latent Variable Model D. Tran, D. Forsyth Configuration Estimates Improve Pedestrian Finding Y. Chen, L. Zhu, C. Lin, A. Yuille, H. Zhang Rapid Inference on a novel AND/OR graph: Detection, Segmentation and Parsing of Articulated Deformable Objects in Cluttered Backgrounds X. Wang, E. Grimson Spatial Latent Dirichlet Allocation S. Boutemedjet, D. Ziou, N. Bouguila A Unified Model for Content Based Image Suggestion and Feature Selection C. Zhang, P. Viola Multiple-Instance Pruning For Learning Efficient Cascade Detectors A. Bouchard-Côté, P. Liang, T. Griffiths, D. Klein A Probabilistic Approach to Language Change S. Petrov, D. Klein Discriminative Log-Linear Grammars with Latent Variables K. Toutanova, M. Johnson A Bayesian LDA-based model for semi-supervised part-of-speech tagging B. Zhao, E. Xing HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation D. Blei, J. McAuliffe Supervised Topic Models E. Linstead, P. Rigor, s. bajracharya, c. lopes, P. Baldi Mining Internet-Scale Software Repositories B. Carterette, R. Jones Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks D. Eck, P. Lamere, T. Bertin-Mahieux, S. Green Automatic Generation of Social Tags for Music Recommendation E. Sudderth, M. Wainwright, A. Willsky Loop Series and Bethe Variational Bounds in Attractive Graphical Models C. Teo, A. Globerson, S. Roweis, A. Smola Convex Learning with Invariances
8:30pm - 12:00am	Demonstrations
	Y. Freund, E. Ettinger, B. McFee, D. Goshorn, S. Shivappa Automatic Cameraman A. Saxena, m. sun, A. Ng Building a 3-D Model From a Single Still Image E. Neftci, E. Chicca, G. Indiveri, J. Slotine, R. Douglas Contraction of VLSI Spiking Neurons K. Gawande, A. Smola, V. S V N, L. Cheng, S. Guenter Elefant W. Xu, K. Yu, Y. Gong Gender and Age Recognition T. Graepel, P. Trelford, R. Herbrich, M. Kochenderfer Learning To Race by Model-Based Reinforcement Learning with Adaptive Abstraction M. Cerf, C. Koch Predicting Human Gaze Using Low-level Saliency Combined with Face Detection P. Forssen, D. Pai Robotic Eye Model with Learning of Pulse-Step Saccades
Tuesday
7:30 - 9:00am	Breakfast
8:00am - 6:00pm	Registration
8:30 - 9:50am	Session 1: Structured Statistical ModelsSession Chair:Michael J Black
	Mark S Handcock Statistical Models for Social Networks with Application to HIV Epidemiology R. Salakhutdinov, A. Mnih Probabilistic Matrix Factorization
9:50 - 10:00am	SpotlightsSpotlights Czar:Stefan Schaal
	M. Weimer, A. Karatzoglou, Q. Le, A. Smola COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking T. Jebara, Y. Song, K. Thadani Density Estimation under Independent Similarly Distributed Sampling Assumptions S. Oba, M. Kawanabe, K. Müller, S. Ishii Heterogeneous Component Analysis R. Silva, W. Chu, Z. Ghahramani Hidden Common Cause Relations in Relational Learning M. Welling, I. Porteous, E. Bart Infinite State Bayes-Nets for Structured Domains Q. Liu, X. Liao, L. Carin Semi-Supervised Multitask Learning P. Ravikumar, H. Liu, J. Lafferty, L. Wasserman SpAM: Sparse Additive Models P. Dangauthier, R. Herbrich, T. Minka, T. Graepel TrueSkill Through Time: Revisiting the History of Chess
10:00 - 10:30am	Break
10:30 - 11:50am	Session 2: Probabilistic OptimizationSession Chair:Francis Bach
	L. Song, A. Smola, K. Borgwardt, A. Gretton Colored Maximum Variance Unfolding P. Mudigonda, V. Kolmogorov, P. Torr An Analysis of Convex Relaxations for MAP Estimation D. Sontag, T. Jaakkola New Outer Bounds on the Marginal Polytope J. Verbeek, B. Triggs Scene Segmentation with CRFs Learned from Partially Labeled Images
11:50am - 12:00pm	SpotlightsSpotlights Czar:Stefan Schaal
	T. van Erven, P. Grunwald, S. de Rooij Catching Up Faster in Bayesian Model Selection and Model Averaging Y. Teh, K. Kurihara, M. Welling Collapsed Variational Inference for HDP D. Sheldon, M. Elmohamed, D. Kozen Collective Inference on Markov Models for Modeling Bird Migration D. Newman, A. Asuncion, P. Smyth, M. Welling Distributed Inference for Latent Dirichlet Allocation M. Szafranski, Y. Grandvalet, P. Morizet-Mahoudeaux Hierarchical Penalization S. Sanghavi, D. Shah, A. Willsky Message Passing for Max-weight Independent Set M. Kearns, J. Tan, J. Vaughan Privacy-Preserving Belief Propagation and Sampling A. Naish-Guzman, S. Holden The Generalized FITC Approximation
12:00 - 2:00pm	Lunch
2:00 - 3:00pm	Yair Censor Projection Methods: Algorithmic Structures, Bregman Projections, and Acceleration Techniques
3:00 - 3:20pm	Peter L Bartlett, Elad Hazan, Alexander Rakhlin Adaptive Online Gradient Descent
3:20 - 3:30pm	SpotlightsSpotlights Czar:Dan Klein
	A. Gretton, K. Fukumizu, C. Teo, L. Song, B. Schölkopf, A. Smola A Kernel Statistical Test of Independence S. Esmeir, S. Markovitch Anytime Induction of Cost-sensitive Trees M. Warmuth, K. Glocer, G. Raetsch Boosting Algorithms for Maximizing the Soft Margin A. Smola, V. S V N, Q. Le Bundle Methods for Machine Learning J. Wright, Y. Tao, Z. Lin, Y. Ma, H. Shum Classification via Minimum Incremental Coding Length (MICL) M. Gashler, D. Ventura, T. Martinez Iterative Non-linear Dimensionality Reduction with Manifold Sculpting P. Li, C. Burges, Q. Wu McRank: Learning to Rank Using Multiple Classification and Gradient Boosting a. rahimi, B. Recht Random Features for Large-Scale Kernel Machines U. von Luxburg, S. Bubeck, S. Jegelka, M. Kaufmann Consistent Minimization of Clustering Objective Functions
3:30 - 4:00pm	Break
4:00 - 5:40pm	Session 3: Theory and Sequential Decision MakingSession Chair:Sanjoy Dasgupta
	J. Bradley, R. Schapire FilterBoost: Regression and Classification on Large Datasets V. Dani, T. Hayes, S. Kakade The Price of Bandit Information for Online Optimization U. Syed, R. Schapire A Multiplicative Weights Algorithm for Apprenticeship Learning O. Shamir, N. Tishby Cluster Stability for Finite Samples
5:20 - 5:30pm	SpotlightsSpotlights Czar:Dan Klein
	S. Dasgupta, D. Hsu, C. Monteleoni A general agnostic active learning algorithm S. Ross, B. Chaib-draa, J. Pineau Bayes-Adaptive POMDPs J. Kolter, P. Abbeel, A. Ng Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion S. Bhatnagar, R. Sutton, M. Ghavamzadeh, M. Lee Incremental Natural Actor-Critic Algorithms G. Tesauro, R. Das, H. Chan, J. Kephart, D. Levine, F. Rawson, C. Lefurgy Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning A. Lazaric, M. Restelli, A. Bonarini Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods A. Krause, H. McMahan, C. Guestrin, A. Gupta Selecting Observations against Adversarial Objectives T. Wang, D. Lizotte, M. Bowling, D. Schuurmans Stable Dual Dynamic Programming D. Hsu, L. Sun, N. Rong What makes some POMDP problems easy to approximate?
7:30pm - 12:00am	Poster Session
	T. Wang, D. Lizotte, M. Bowling, D. Schuurmans Stable Dual Dynamic Programming S. Ross, B. Chaib-draa, J. Pineau Bayes-Adaptive POMDPs D. Hsu, L. Sun, N. Rong What makes some POMDP problems easy to approximate? S. Bhatnagar, R. Sutton, M. Ghavamzadeh, M. Lee Incremental Natural Actor-Critic Algorithms A. Lazaric, M. Restelli, A. Bonarini Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods A. Antos, R. Munos, C. Szepesvari Fitted Q-iteration in continuous action-space MDPs M. Hutter, S. Legg Temporal Difference with Eligibility Traces Derived from First Principles G. Tesauro, R. Das, H. Chan, J. Kephart, D. Levine, F. Rawson, C. Lefurgy Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning J. Kolter, P. Abbeel, A. Ng Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion U. Syed, R. Schapire A Multiplicative Weights Algorithm for Apprenticeship Learning V. Dani, T. Hayes, S. Kakade The Price of Bandit Information for Online Optimization P. Bartlett, E. Hazan, A. Rakhlin Adaptive Online Gradient Descent S. Dasgupta, D. Hsu, C. Monteleoni A general agnostic active learning algorithm J. Audibert Progressive mixture rules are deviation suboptimal M. Warmuth, K. Glocer, G. Raetsch Boosting Algorithms for Maximizing the Soft Margin K. Pelckmans, J. Suykens, B. De Moor A Risk Minimization Principle for a Class of Parzen Estimators A. Smola, V. S V N, Q. Le Bundle Methods for Machine Learning U. von Luxburg, S. Bubeck, S. Jegelka, M. Kaufmann Consistent Minimization of Clustering Objective Functions O. Shamir, N. Tishby Cluster Stability for Finite Samples R. Salakhutdinov, A. Mnih Probabilistic Matrix Factorization M. Weimer, A. Karatzoglou, Q. Le, A. Smola COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking L. Song, A. Smola, K. Borgwardt, A. Gretton Colored Maximum Variance Unfolding M. Gashler, D. Ventura, T. Martinez Iterative Non-linear Dimensionality Reduction with Manifold Sculpting G. Burghouts, A. Smeulders, J. Geusebroek The Distribution Family of Similarity Distances S. Oba, M. Kawanabe, K. Müller, S. Ishii Heterogeneous Component Analysis J. Ye, Z. Zhao, M. Wu Discriminative K-means for Clustering F. Bach, Z. Harchaoui DIFFRAC: a discriminative and flexible framework for clustering K. Chen, S. Wang Regularized Boost for Semi-Supervised Learning Q. Liu, X. Liao, L. Carin Semi-Supervised Multitask Learning Z. Xu, R. Jin, J. Zhu, I. King, M. Lyu Efficient Convex Relaxation for Transductive Support Vector Machine K. Kumar, C. Bhattacharyya, R. Hariharan A Randomized Algorithm for Large Scale Support Vector Learning a. rahimi, B. Recht Random Features for Large-Scale Kernel Machines T. Kato, H. Kashima, M. Sugiyama, K. Asai Multi-Task Learning via Conic Programming S. Esmeir, S. Markovitch Anytime Induction of Cost-sensitive Trees E. Chang, K. Zhu, H. Wang, h. Bai, J. Li, Z. Qiu, H. Cui Parallelizing Support Vector Machines on Distributed Computers J. Bradley, R. Schapire FilterBoost: Regression and Classification on Large Datasets P. Li, C. Burges, Q. Wu McRank: Learning to Rank Using Multiple Classification and Gradient Boosting Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, G. Sun A General Boosting Method and its Application to Learning Ranking Functions for Web Search J. Wright, Y. Tao, Z. Lin, Y. Ma, H. Shum Classification via Minimum Incremental Coding Length (MICL) C. Gentile, F. Vitale, C. Brotto On higher-order perceptron algorithms M. Szafranski, Y. Grandvalet, P. Morizet-Mahoudeaux Hierarchical Penalization D. Sontag, T. Jaakkola New Outer Bounds on the Marginal Polytope P. Mudigonda, V. Kolmogorov, P. Torr An Analysis of Convex Relaxations for MAP Estimation S. Sanghavi, D. Shah, A. Willsky Message Passing for Max-weight Independent Set M. Kearns, J. Tan, J. Vaughan Privacy-Preserving Belief Propagation and Sampling J. Verbeek, B. Triggs Scene Segmentation with CRFs Learned from Partially Labeled Images M. Mahdaviani, T. Choudhury Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition D. Sheldon, M. Elmohamed, D. Kozen Collective Inference on Markov Models for Modeling Bird Migration P. Dangauthier, R. Herbrich, T. Minka, T. Graepel TrueSkill Through Time: Revisiting the History of Chess J. Hernández-Lobato, T. Dijkstra, T. Heskes Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach C. Archambeau, M. Opper, Y. Shen, D. Cornford, J. Shawe-Taylor Variational Inference for Diffusion Processes M. Opper, G. Sanguinetti Variational inference for Markov jump processes Y. Teh, K. Kurihara, M. Welling Collapsed Variational Inference for HDP D. Newman, A. Asuncion, P. Smyth, M. Welling Distributed Inference for Latent Dirichlet Allocation M. Welling, I. Porteous, E. Bart Infinite State Bayes-Nets for Structured Domains M. Titsias The Infinite Gamma-Poisson Feature Model M. Shashanka, B. Raj, P. Smaragdis Sparse Overcomplete Latent Variable Decomposition of Counts Data T. Jebara, Y. Song, K. Thadani Density Estimation under Independent Similarly Distributed Sampling Assumptions M. Sugiyama, S. Nakajima, H. Kashima, P. von Buenau, M. Kawanabe Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation T. van Erven, P. Grunwald, S. de Rooij Catching Up Faster in Bayesian Model Selection and Model Averaging R. Silva, W. Chu, Z. Ghahramani Hidden Common Cause Relations in Relational Learning S. Zhu, K. Yu, Y. Gong Predictive Matrix-Variate t Models P. Ravikumar, H. Liu, J. Lafferty, L. Wasserman SpAM: Sparse Additive Models A. Naish-Guzman, S. Holden The Generalized FITC Approximation K. Yu, W. Chu Gaussian Process Models for Link Analysis and Transfer Learning A. Naish-Guzman, S. Holden Robust Regression with Twinned Gaussian Processes A. Krause, H. McMahan, C. Guestrin, A. Gupta Selecting Observations against Adversarial Objectives A. Gretton, K. Fukumizu, C. Teo, L. Song, B. Schölkopf, A. Smola A Kernel Statistical Test of Independence Z. Harchaoui, F. Bach, M. Eric Testing for Homogeneity with Kernel Fisher Discriminant Analysis A. Lecchini-Visintini, J. Lygeros, J. Maciejowski Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
7:30pm - 12:00am	Demonstrations
	K. Adiloglu, R. Anniés, Y. Visell, K. Franinovic, C. Drioli Adaptive Bottle R. Hearn, R. Granger Basal-ganglia-inspired Hierarchical Reinforcement Learning in an AIBO robot A. Saffari Azar Alamdari, I. Guyon, H. Escalante, G. Bakir, G. Cawley CLOP: a Matlab Learning Object Package S. Gould, M. Quigley, A. Ng, D. Koller Holistic Scene Understanding from Visual and Range Data M. van de Panne, K. Yin, S. Coros, K. Loken Robust Biped Locomotion Using Simple Low-dimensional Control Policies D. Touretzky, E. Tira-Thompson Tekkotsu Cognitive Robotics E. Tsang, S. LAM, B. Shi Visualization of DepthMotion Perception by Model Cortical Neurons
Wednesday
7:30 - 9:00am	Breakfast
8:00am - 6:00pm	Registration
8:30 - 9:50am	Session 4: Probabilistic Models and MethodsSession Chair:William S Noble
	David Heckerman Graphical models for HIV vaccine design and genome-wide association studies Y. Teh, H. Daume III, D. Roy Bayesian Agglomerative Clustering with Coalescents
9:50 - 10:00am	SpotlightsSpotlights Czar:Ulrike von Luxburg
	P. Liang, D. Klein, M. Jordan Agreement-Based Learning F. Sinz, O. Chapelle, A. Agarwal, B. Schölkopf An Analysis of Inference with the Universum N. Chapados, Y. Bengio Augmented Functional Time Series Representation and Forecasting with Gaussian Processes S. Yu, B. Krishnapuram, R. Rosales, H. Steck, R. Rao Bayesian Multi-View Learning L. Sigal, A. Balan, M. Black Combined discriminative and generative articulated pose and non-rigid shape estimation H. Chieu, L. Sun, Y. Teh Cooled and Relaxed Survey Propagation for MRFs O. Sumer, U. Acar, A. Ihler, R. Mettu Efficient Bayesian Inference for Dynamically Changing Graphs K. Ganchev, J. Graca, B. Taskar Expectation Maximization, Posterior Constraints, and Statistical Alignment E. Bonilla, K. Chai, C. Williams Multi-task Gaussian Process Prediction A. Lecchini-Visintini, J. Lygeros, J. Maciejowski Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
10:00 - 10:30am	Break
10:30 - 11:50am	Session 5: Probabilistic Representations and LearningSession Chair:Yee Whye Teh
	S. Kirshner Learning with Tree-Averaged Densities and Distributions G. Lebanon, Y. Mao Non-parametric Modeling of Partially Ranked Data J. Huang, C. Guestrin, L. Guibas Efficient Inference forDistributions on Permutations D. Wingate, S. Singh Exponential Family Predictive Representations of State
11:50am - 12:00pm	SpotlightsSpotlights Czar:Ulrike von Luxburg
	A. Argyriou, C. Micchelli, M. Pontil, Y. Ying A Spectral Regularization Framework for Multi-Task Structure Learning T. Sharpee Better than least squares: comparison of objective functions for estimating linear-nonlinear models V. Singh, L. Mukherjee, J. Peng, J. Xu Ensemble Clustering using Semidefinite Programming M. Mozer, D. Baldwin Experience-Guided Search: A Theory of Attentional Control A. Christmann, I. Steinwart How SVMs can estimate quantiles and the median K. Fukumizu, A. Gretton, X. Sun, B. Schölkopf Kernel Measures of Conditional Dependence J. He, J. Carbonell Nearest-Neighbor-Based Active Learning for Rare Category Detection Z. Barutcuoglu, P. Long, R. Servedio One-Pass Boosting M. Mahmud, S. Ray Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations
12:00 - 2:00pm	Lunch
2:00 - 3:20pm	Session 6: Cognitive ProcessesSession Chair:Mark Steyvers
	Elizabeth Spelke Core Knowledge of Number and Geometry A. Sanborn, T. Griffiths Markov Chain Monte Carlo with People
3:20 - 3:30pm	SpotlightsSpotlights Czar:Chris Williams
	M. Frank, N. Goodman, J. Tenenbaum A Bayesian Framework for Cross-Situational Word-Learning U. Beierholm, K. Kording, L. Shams, W. Ma Comparing Bayesian models for multisensory cue combination without mandatory integration R. Peters, L. Itti Congruence between model and human attention reveals unique signatures of critical visual events V. Ferrari, A. Zisserman Learning Visual Attributes B. Russell, A. Torralba, C. Liu, R. Fergus, W. Freeman Object Recognition by Scene Alignment V. Rao, M. Howard Retrieved context and the discovery of semantic structure P. Frazier, A. Yu Sequential Hypothesis Testing under Stochastic Deadlines M. Figueroa, G. Carvajal, W. Valenzuela Subspace-Based Face Recognition in Analog VLSI R. Legenstein, D. Pecevski, W. Maass Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
3:30 - 4:00pm	Break
4:00 - 5:30pm	Session 7: Systems and ApplicationsSession Chair:Fei Sha
	S. Siddiqi, B. Boots, G. Gordon A Constraint Generation Approach to Learning Stable Linear Dynamical Systems D. Mochihashi, E. Sumita The Infinite Markov Model G. Englebienne, T. Cootes, M. Rattray A probabilistic model for generating realistic lip movements from speech Y. Lin, J. Chen, Y. Kim, D. Lee Blind channel identification for speech dereverberation using l1-norm sparse learning
5:20 - 5:30pm	SpotlightsSpotlights Czar:Chris Williams
	D. Sridharan, B. Percival, j. arthur, K. Boahen An in-silico Neural Model of Dynamic Routing through Neuronal Coherence E. Neftci, E. Chicca, G. Indiveri, J. Slotine, R. Douglas Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons S. LAM, B. Shi Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination J. Cunningham, B. Yu, K. Shenoy, M. Sahani Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes B. Blankertz, M. Kawanabe, R. Tomioka, F. Hohlefeld, V. Nikulin, K. Müller Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing J. Dauwels, F. Vialatte, T. Rutkowski, A. CICHOCKI Measuring Neural Synchrony by Message Passing M. Bethge, P. Berens Near-Maximum Entropy Models for Binary Neural Representations of Natural Images N. Daw, A. Courville The rat as particle filter
7:30pm - 12:00am	Poster Session
	M. Mozer, D. Baldwin Experience-Guided Search: A Theory of Attentional Control A. Yuille, H. Lu The Noisy-Logical Distribution and its Application to Causal Inference Z. Barutcuoglu, P. Long, R. Servedio One-Pass Boosting V. Singh, L. Mukherjee, J. Peng, J. Xu Ensemble Clustering using Semidefinite Programming K. Fukumizu, A. Gretton, X. Sun, B. Schölkopf Kernel Measures of Conditional Dependence A. Christmann, I. Steinwart How SVMs can estimate quantiles and the median T. Sharpee Better than least squares: comparison of objective functions for estimating linear-nonlinear models M. Mahmud, S. Ray Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations A. Argyriou, C. Micchelli, M. Pontil, Y. Ying A Spectral Regularization Framework for Multi-Task Structure Learning J. He, J. Carbonell Nearest-Neighbor-Based Active Learning for Rare Category Detection P. Liang, D. Klein, M. Jordan Agreement-Based Learning K. Ganchev, J. Graca, B. Taskar Expectation Maximization, Posterior Constraints, and Statistical Alignment Y. Teh, H. Daume III, D. Roy Bayesian Agglomerative Clustering with Coalescents S. Kirshner Learning with Tree-Averaged Densities and Distributions D. Endres, M. Oram, J. Schindelin, P. Foldiak Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms H. Chieu, L. Sun, Y. Teh Cooled and Relaxed Survey Propagation for MRFs S. Yu, B. Krishnapuram, R. Rosales, H. Steck, R. Rao Bayesian Multi-View Learning O. Sumer, U. Acar, A. Ihler, R. Mettu Efficient Bayesian Inference for Dynamically Changing Graphs F. Sinz, O. Chapelle, A. Agarwal, B. Schölkopf An Analysis of Inference with the Universum G. Lebanon, Y. Mao Non-parametric Modeling of Partially Ranked Data J. Huang, C. Guestrin, L. Guibas Efficient Inference forDistributions on Permutations E. Bonilla, K. Chai, C. Williams Multi-task Gaussian Process Prediction N. Chapados, Y. Bengio Augmented Functional Time Series Representation and Forecasting with Gaussian Processes S. Siddiqi, B. Boots, G. Gordon A Constraint Generation Approach to Learning Stable Linear Dynamical Systems D. Wingate, S. Singh Exponential Family Predictive Representations of State D. Mochihashi, E. Sumita The Infinite Markov Model A. Graves, S. Fernandez, M. Liwicki, H. Bunke, J. Schmidhuber Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks G. Englebienne, T. Cootes, M. Rattray A probabilistic model for generating realistic lip movements from speech Y. Lin, J. Chen, Y. Kim, D. Lee Blind channel identification for speech dereverberation using l1-norm sparse learning R. Turner, M. Sahani Modeling Natural Sounds with Modulation Cascade Processes U. Beierholm, K. Kording, L. Shams, W. Ma Comparing Bayesian models for multisensory cue combination without mandatory integration R. Peters, L. Itti Congruence between model and human attention reveals unique signatures of critical visual events A. Sanborn, T. Griffiths Markov Chain Monte Carlo with People M. Frank, N. Goodman, J. Tenenbaum A Bayesian Framework for Cross-Situational Word-Learning C. Kemp, N. Goodman, J. Tenenbaum A complexity measure for intuitive theories P. Frazier, A. Yu Sequential Hypothesis Testing under Stochastic Deadlines V. Rao, M. Howard Retrieved context and the discovery of semantic structure N. Daw, A. Courville The rat as particle filter B. Fischer Optimal models of sound localization by barn owls B. Williams, M. Toussaint, A. Storkey Modelling motion primitives and their timing in biologically executed movements B. Russell, A. Torralba, C. Liu, R. Fergus, W. Freeman Object Recognition by Scene Alignment V. Ferrari, A. Zisserman Learning Visual Attributes L. Sigal, A. Balan, M. Black Combined discriminative and generative articulated pose and non-rigid shape estimation N. Le Roux, Y. Bengio, P. Lamblin, M. Joliveau, B. Kégl Learning the 2-D Topology of Images M. Parsana, S. Bhattacharya, C. Bhattacharyya, K. Ramakrishnan Kernels on Attributed Pointsets with Applications M. Bethge, P. Berens Near-Maximum Entropy Models for Binary Neural Representations of Natural Images P. Berkes, R. Turner, M. Sahani On Sparsity and Overcompleteness in Image Models D. Gao, V. Mahadevan, N. Vasconcelos The discriminant center-surround hypothesis for bottom-up saliency M. Cerf, J. Harel, W. Einhäuser, C. Koch Predicting human gaze using low-level saliency combined with face detection M. Ross, A. Cohen GRIFT: A graphical model for inferring visual classification features from human data H. Lee, E. Chaitanya, A. Ng Sparse deep belief net model for visual area V2 E. Tsang, B. Shi Estimating disparity with confidence from energy neurons P. Garrigues, B. Olshausen Learning Horizontal Connections in a Sparse Coding Model of Natural Images A. Stocker, E. Simoncelli A Bayesian Model of Conditioned Perception J. Macke, G. Zeck, M. Bethge Receptive Fields without Spike-Triggering C. Clopath, A. Longtin, W. Gerstner An online Hebbian learning rule that performs Independent Component Analysis S. Gerwinn, J. Macke, M. Seeger, M. Bethge Bayesian Inference for Spiking Neuron Models with a Sparsity Prior O. Bobrowski, R. Meir, S. Shoham, Y. Eldar A neural network implementing optimal state estimation based on dynamic spike train decoding M. Lengyel, P. Dayan Hippocampal Contributions to Control: The Third Way J. Dauwels, F. Vialatte, T. Rutkowski, A. CICHOCKI Measuring Neural Synchrony by Message Passing J. Pillow, P. Latham Neural characterization in partially observed populations of spiking neurons L. Buesing, W. Maass Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons S. LAM, B. Shi Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination R. Legenstein, D. Pecevski, W. Maass Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity M. Ahrens, M. Sahani Inferring Elapsed Time from Stochastic Neural Processes J. Cunningham, B. Yu, K. Shenoy, M. Sahani Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes B. Blankertz, M. Kawanabe, R. Tomioka, F. Hohlefeld, V. Nikulin, K. Müller Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing P. Ferrez, J. Millán EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection C. Christoforou, P. Sajda, L. Parra Second Order Bilinear Discriminant Analysis for single trial EEG analysis S. Ghebreab, A. Smeulders, P. Adriaans Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression F. Meyer, G. Stephens Locality and low-dimensions in the prediction of natural experience from fMRI L. Murray, A. Storkey Continuous Time Particle Filtering for fMRI D. Sridharan, B. Percival, j. arthur, K. Boahen An in-silico Neural Model of Dynamic Routing through Neuronal Coherence S. Mitra, G. Indiveri, S. Fusi Learning to classify complex patterns using a VLSI network of spiking neurons M. Giulioni, m. pannunzi, D. Badoni, V. Dante, p. del giudice A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses E. Neftci, E. Chicca, G. Indiveri, J. Slotine, R. Douglas Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons M. Figueroa, G. Carvajal, W. Valenzuela Subspace-Based Face Recognition in Analog VLSI
Thursday
7:30 - 9:00am	Breakfast
8:00am - 1:00pm	Registration (Hyatt)
8:30 - 10:10am	Session 8: Neuroscience ISession Chair:Alan A Stocker
	M. Ahrens, M. Sahani Inferring Elapsed Time from Stochastic Neural Processes O. Bobrowski, R. Meir, S. Shoham, Y. Eldar A neural network implementing optimal state estimation based on dynamic spike train decoding S. Gerwinn, J. Macke, M. Seeger, M. Bethge Bayesian Inference for Spiking Neuron Models with a Sparsity Prior J. Pillow, P. Latham Neural characterization in partially observed populations of spiking neurons S. Mitra, G. Indiveri, S. Fusi Learning to classify complex patterns using a VLSI network of spiking neurons
10:10 - 10:40am	Break
10:40am - 12:00pm	Session 9: Neuroscience IISession Chair:Odelia Schwartz
	M. Lengyel, P. Dayan Hippocampal Contributions to Control: The Third Way Manabu Tanifuji Population coding of object images based on visual features and its relevance to view invariant representation
5:00 - 8:30pm	Registration (Westin)

Workshops (December 7-8, 2007)

The post-Conference Workshops will be held at the Westin Resort and Spa and the Westin Hilton in Whistler, British Columbia, Canada on December 7 and 8, 2007.

The Workshops provide multi-track intensive sessions on a wide range of topics. The venue and schedule facilitate informality and depth. Please see a list of Workshops below, along with links to further information for each.

Thursday
6:30 - 8:30pm	Welcoming Reception
Friday
6:30 - 8:00am	Breakfast
7:30am - 6:30pm	Matthias W Seeger, David Barber, Neil D Lawrence, Onno Zoeter Approximate Bayesian Inference in Continuous/Hybrid Models
7:30am - 6:30pm	Richard E Turner, Pietro Berkes, Maneesh Sahani Beyond Simple Cells: Probabilistic Models for Visual Cortical Processing
7:30am - 6:30pm	Samy Bengio, Corinna Cortes, Dennis DeCoste, Francois Fleuret, Ramesh Natarajan, Edwin Pednault, Dan Pelleg, Elad Yom-Tov Efficient Machine Learning - Overcoming Computational Bottlenecks in Machine Learning (Part 1)
7:30am - 6:30pm	Ryan Canolty, Kai J Miller Large Scale Brain Dynamics (Part 1)
7:30am - 6:30pm	Archana Ganapathi, Sumit Basu, Fei Sha, Emre Kiciman Machine Learning for Systems Problems (Part 1)
7:30am - 6:30pm	Dengyong Zhou, Olivier Chapelle, Thorsten Joachims, Thomas Hofmann Machine Learning for Web Search
7:30am - 6:30pm	Gal Chechik, Christina S Leslie, Quaid D Morris, William S Noble, Gunnar Raetsch, Koji Tsuda Machine Learning in Computational Biology (Part 1)
7:30am - 6:30pm	David R Hardoon, Eduardo Reck-Miranda, John S Shawe-Taylor Music, Brain and Cognition. Part 1: Learning the Structure of Music and Its Effects On the Brain
7:30am - 6:30pm	John Langford, Alina Beygelzimer Principles of Learning Problem Design
7:30am - 6:30pm	Jan R Peters, Marc Toussaint Robotics Challenges for Machine Learning
7:30am - 6:30pm	Michael Aupetit, Frederic Chazal, Gilles Gasso, David Cohen-Steiner, pierre gaillard Topology Learning: New Challenges At the Crossing of Machine Learning,
9:00am - 6:00pm	Yael Niv, Matthew Botvinick, Andrew G Barto Hierarchical Organization of Behavior: Computational, Psychological and Neural Perspectives (Part 1)
3:00 - 7:00pm	Registration
7:00 - 9:00pm	IBM Student Reception
Saturday
6:30 - 8:00am	Breakfast
7:30am - 6:30pm	Samy Bengio, Corinna Cortes, Dennis DeCoste, Francois Fleuret, Ramesh Natarajan, Edwin Pednault, Dan Pelleg, Elad Yom-Tov Efficient Machine Learning - Overcoming Computational Bottlenecks in Machine Learning (Part 2)
7:30am - 6:30pm	Ryan Canolty, Kai J Miller Large Scale Brain Dynamics (Part 2)
7:30am - 6:30pm	Archana Ganapathi, Sumit Basu, Fei Sha, Emre Kiciman Machine Learning for Systems Problems (Part 2)
7:30am - 6:30pm	Gal Chechik, Christina S Leslie, Quaid D Morris, William S Noble, Gunnar Raetsch, Koji Tsuda Machine Learning in Computational Biology (Part 2)
7:30am - 6:30pm	Hendrik Purwins, Xavier Serra, Klaus H Obermayer Music, Brain and Cognition. Part 2: Models of Sound and Cognition
7:30am - 6:30pm	Kenji Fukumizu, Arthur Gretton, Alexander J Smola Representations and Inference on Probability Distributions
7:30am - 6:30pm	Kevin Murphy, Lise Getoor, Eric P Xing, Raphael Gottardo Statistical Network Models
7:30am - 6:30pm	Virginia Savova, Josh Tenenbaum, Leslie Kaelbling, Alan L Yuille The Grammar of Vision: Probabilistic Grammar-Based Models for Visual Scene Understanding and Object Categorization
8:30am - 5:30pm	Jillian H Fecteau, Dirk B Walther, Vidhya Navalpakkam, John K Tsotsos Mechanisms of Visual Attention
9:00am - 5:00pm	Joaquin Quiñonero Candela, Thore K Graepel, Ralf Herbrich Machine Learning and Games (MALAGA): Open Directions in Applying Machine Learning to Games
9:00am - 5:00pm	Richard Lippman, Pavel Laskov Machine Learning in Adversarial Environments for Computer Security
9:00am - 5:00pm	Sebastian Thrun, Chris Urmson, Raul Rojas, William Uther The Urban Challenge – Perspectives of Autonomous Driving
9:00am - 6:00pm	Yael Niv, Matthew Botvinick, Andrew G Barto Hierarchical Organization of Behavior: Computational, Psychological and Neural Perspectives (Part 2)
7:30 - 10:00pm	Banquet and Wrap up Meeting

The schedule was last modified on 2012-12-2 13:55 PST