Schedule

Tutorials (December 12, 2011)

Sunday, December 11
4:00 - 8:00pm	Registration at Congress Center
Monday, December 12
7:30am - 6:30pm	Registration Desk at Congress Center
9:30 - 11:30am	Jonathan Pillow Flexible, Multivariate Point Process Models for Unlocking the Neural Code
9:30 - 11:30am	Amir Globerson, Tommi Jaakkola Linear Programming Relaxations for Graphical Models
11:30am - 12:00pm	Coffee Break
12:00 - 2:00pm	Alexander Rush, Michael Collins Lagrangian Relaxation Algorithms for Inference in Natural Language Processing
12:00 - 2:00pm	Peter Orbanz, Yee Whye Teh Modern Bayesian Nonparametrics
2:00 - 4:00pm	Break
4:00 - 6:00pm	Amr Ahmed, Alexander Smola Graphical Models for the Internet
4:00 - 6:00pm	Naftali Tishby Information Theory in Learning and Control
6:00 - 6:30pm	Break

Conference Sessions (December 12-15, 2011)

Monday, December 12
6:30 - 6:40pm	Terrence Sejnowski, Peter Bartlett, Fernando Pereira Opening Remarks and Awards
6:40 - 7:00pm	Spotlight Session 1Session Chair:Rob Fergus
	J. Hirayama, A. Hyvarinen Structural equations and divisive normalization for energy-dependent component analysis Y. Watanabe Uniqueness of Belief Propagation on Signed Graphs R. Turner, M. Sahani Probabilistic amplitude and frequency demodulation A. Juditsky, F. Kilinc Karzan, A. Nemirovski, B. Polyak On the accuracy of l1-filtering of signals with block-sparse structure B. Ujfalussy, M. Lengyel Active dendrites: adaptation to spike-based communication
7:00 - 11:59pm	Poster Session and Reception
	P. Wagner M001 A reinterpretation of the policy oscillation phenomenon in approximate policy iteration J. Choi, K. Kim M002 MAP Inference for Bayesian Inverse Reinforcement Learning Z. Lim, D. Hsu, L. Sun M003 Monte Carlo Value Iteration with Macro-Actions A. Barreto, D. Precup, J. Pineau M004 Reinforcement Learning using Kernel-Based Stochastic Factorization P. Thomas M005 Policy Gradient Coagent Networks T. Zhao, H. Hachiya, G. Niu, M. Sugiyama M006 Analysis and Improvement of Policy Gradient Estimation J. Messias, M. Spaan, P. Lima M007 Efficient Offline Communication Policies for Factored Multiagent POMDPs M. Gheshlaghi Azar, R. Munos, M. Ghavamzadeh, H. Kappen M008 Speedy Q-Learning P. Hennig M009 Optimal Reinforcement Learning for Gaussian Systems S. Niekum, A. Barto M010 Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery S. Levine, Z. Popovic, V. Koltun M011 Nonlinear Inverse Reinforcement Learning with Gaussian Processes S. Mensi, R. Naud, W. Gerstner M012 From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models J. Dethier, P. Nuyujukian, C. Eliasmith, T. Stewart, S. Elasaad, K. Shenoy, K. Boahen M013 A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm M. Stemmler, B. Sengupta, S. Laughlin, J. Niven M014 Energetically Optimal Action Potentials B. Ujfalussy, M. Lengyel M015 Active dendrites: adaptation to spike-based communication I. Stevenson, K. Koerding M016 Inferring spike-timing-dependent plasticity from spike train data B. Petreska, B. Yu, J. Cunningham, G. Santhanam, S. Ryu, K. Shenoy, M. Sahani M017 Dynamical segmentation of single trials from population neural data M. Keil M018 Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits J. Leibo, J. Mutch, T. Poggio M019 Why The Brain Separates Face Recognition From Object Recognition R. Kobayashi, Y. Tsubo, P. Lansky, S. Shinomoto M020 Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron J. Macke, I. Murray, P. Latham M021 How biased are maximum entropy models? A. Geiger, C. Wojek, R. Urtasun M022 Joint 3D Estimation of Objects and Scene Layout V. Lempitsky, A. Vedaldi, A. Zisserman M023 Pylon Model for Semantic Segmentation S. Kim, S. Nowozin, P. Kohli, C. Yoo M024 Higher-Order Correlation Clustering for Image Segmentation A. Saxe, M. Bhand, R. Mudur, B. Suresh, A. Ng M025 Unsupervised learning models of primary cortical receptive fields and receptive field plasticity J. Lim, R. Salakhutdinov, A. Torralba M026 Transfer Learning by Borrowing Examples L. Bo, X. Ren, D. Fox M028 Hierarchical Matching Pursuit for Recognition: Architecture and Fast Algorithms F. Khan, J. van de Weijer, A. Bagdanov, M. Vanrell M029 Portmanteau Vocabularies for Multi-Cue Image Representation A. Bergamo, L. Torresani, A. Fitzgibbon M030 PiCoDes: Learning a Compact Code for Novel-Category Recognition P. Jain, A. Tewari, I. Dhillon M031 Orthogonal Matching Pursuit with Replacement A. Waters, A. Sankaranarayanan, R. Baraniuk M032 SpaRCS: Recovering low-rank and sparse matrices from compressive measurements S. Kurtek, A. Srivastava, W. Wu M033 Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment M. Sorower, T. Dietterich, J. Doppa, W. Orr, P. Tadepalli, X. Fern M034 Inverting Grice's Maxims to Learn Rules from Natural Language Extractions P. Dhillon, D. Foster, L. Ungar M035 Multi-View Learning of Word Embeddings via CCA K. Jamieson, R. Nowak M036 Active Ranking using Pairwise Comparisons M. Chen, K. Weinberger, J. Blitzer M037 Co-Training for Domain Adaptation K. Sricharan, A. Hero M038 Efficient anomaly detection using bipartite k-NN graphs H. Wang, H. Huang, F. Kamangar, F. Nie, C. Ding M039 A Maximum Margin Multi-Instance Learning Framework for Image Categorization G. Kunapuli, R. Maclin, J. Shavlik M040 Advice Refinement in Knowledge-Based SVMs M. Saberian, N. Vasconcelos M041 Multiclass Boosting: Theory and Algorithms C. Dubout, F. Fleuret M042 Boosting with Maximum Adaptive Sampling N. Goernitz, C. Widmer, G. Zeller, A. Kahles, S. Sonnenburg, G. Raetsch M044 Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation J. Petterson, T. Caetano M045 Submodular Multi-Label Learning J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl M046 Algorithms for Hyper-Parameter Optimization V. Sindhwani, A. Lozano M047 Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels N. Shroff, P. Turaga, R. Chellappa M048 Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds L. Xiong, B. Poczos, J. Schneider M049 Group Anomaly Detection using Flexible Genre Models R. Cabral, F. De la Torre, J. Costeira, A. Bernardino M050 Matrix Completion for Image Classification A. Coates, A. Ng M051 Selecting Receptive Fields in Deep Networks A. Kumar, P. Rai, H. Daume III M052 Co-regularized Multi-view Spectral Clustering R. Foygel, R. Salakhutdinov, O. Shamir, N. Srebro M053 Learning with the weighted trace-norm under arbitrary sampling distributions L. Yang M054 Active Learning with a Drifting Distribution D. Adametz, V. Roth M055 Bayesian Partitioning of Large-Scale Distance Data M. Hein, S. Setzer M056 Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts J. Hirayama, A. Hyvarinen M057 Structural equations and divisive normalization for energy-dependent component analysis K. Yılmaz, A. Cemgil, U. Simsekli M058 Generalised Coupled Tensor Factorisation P. Kar, P. Jain M059 Similarity-based Learning via Data Driven Embeddings J. Wang, H. Do, A. Woznica, A. Kalousis M060 Metric Learning with Multiple Kernels P. Perry, M. Mahoney M061 Regularized Laplacian Estimation and Fast Eigenvector Approximation X. Zhang, D. Dunson, L. Carin M062 Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices S. Kakade, A. Kalai, V. Kanade, O. Shamir M063 Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression S. Lyu M064 Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction R. Tomioka, T. Suzuki, K. Hayashi, H. Kashima M065 Statistical Performance of Convex Tensor Decomposition A. Juditsky, F. Kilinc Karzan, A. Nemirovski, B. Polyak M066 On the accuracy of l1-filtering of signals with block-sparse structure L. Bui, R. Johari, S. Mannor M067 Committing Bandits E. Hazan, S. Kale M068 Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction W. Koolen, W. Kotlowski, M. Warmuth M069 Learning Eigenvectors for Free A. Rakhlin, K. Sridharan, A. Tewari M070 Online Learning: Stochastic, Constrained, and Smoothed Adversaries R. Munos M071 Optimistic Optimization of Deterministic Functions L. Oneto, D. Anguita, A. Ghio, S. Ridella M072 The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers T. Suzuki M073 Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning I. Dhillon, P. Ravikumar, A. Tewari M074 Nearest Neighbor based Greedy Coordinate Descent Y. Wiener, R. El-Yaniv M075 Agnostic Selective Classification D. Dai, T. Zhang M076 Greedy Model Averaging D. Choi, P. Wolfe, E. Airoldi M077 Confidence Sets for Network Structure G. Desjardins, A. Courville, Y. Bengio M078 On Tracking The Partition Function R. Turner, M. Sahani M079 Probabilistic amplitude and frequency demodulation M. Titsias, M. Lázaro-Gredilla M081 Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning R. Silva M082 Thinning Measurement Models and Questionnaire Design P. Olmos, L. Salamanca, J. Murillo Fuentes, F. Perez-Cruz M083 An Application of Tree-Structured Expectation Propagation for Channel Decoding S. Nakajima, M. Sugiyama, S. Babacan M084 Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Y. Zhang, C. Sutton M085 Quasi-Newton Methods for Markov Chain Monte Carlo D. Wingate, N. Goodman, A. Stuhlmueller, J. Siskind M086 Nonstandard Interpretations of Probabilistic Programs for Efficient Inference Y. Watanabe M087 Uniqueness of Belief Propagation on Signed Graphs D. Knowles, T. Minka M088 Non-conjugate Variational Message Passing for Multinomial and Binary Regression V. Raykar, S. Yu M089 Ranking annotators for crowdsourced labeling tasks V. Rao, Y. Teh M090 Gaussian process modulated renewal processes J. Zhu, N. Chen, E. Xing M091 Infinite Latent SVM for Classification and Multi-task Learning S. Ghosh, A. Ungureanu, E. Sudderth, D. Blei M092 Spatial distance dependent Chinese Restaurant Process for image segmentation A. Susemihl, R. Meir, M. Opper M093 Analytical Results for the Error in Filtering of Gaussian Processes D. Hernández-lobato, J. Hernández-Lobato, P. Dupont M094 Robust Multi-Class Gaussian Process Classification D. Duvenaud, H. Nickisch, C. Rasmussen M095 Additive Gaussian Processes A. Tofigh, E. Sj̦lund, M. H̦glund, J. Lagergren M096 A Global Structural EM Algorithm for a Model of Cancer Progression D. Sheldon, T. Dietterich M097 Collective Graphical Models T. Pham, T. Chin, J. Yu, D. Suter M098 Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC J. Mooij, D. Janzing, T. Heskes, B. Schölkopf M099 Causal Discovery with Cyclic Additive Noise Models A. Gunawardana, C. Meek, P. Xu M100 A Model for Temporal Dependencies in Event Streams M. Zeiler, G. Taylor, L. Sigal, I. Matthews, R. Fergus M101 Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines T. Huang, J. Schneider M102 Learning Auto-regressive Models from Sequence and Non-sequence Data F. Bach M103 Shaping Level Sets with Submodular Functions B. Zhao, F. Li, E. Xing M104 Large-Scale Category Structure Aware Image Categorization L. Song, A. Parikh, E. Xing M105 Kernel Embeddings of Latent Tree Graphical Models
Tuesday, December 13
8:00am - 5:30pm	Registration Desk
9:30 - 10:40am	Oral Session 1Session Chair:Remi Munos
	R. Sutton Learning About Sensorimotor Data O. Kroemer, J. Peters A Non-Parametric Approach to Dynamic Programming
10:40 - 11:10am	Spotlight Session 2Session Chair:Remi Munos
	A. Farahmand Action-Gap Phenomenon in Reinforcement Learning J. Kolter The Fixed Points of Off-Policy TD C. Kemp Inductive reasoning about chimeric creatures A. Jern, C. Lucas, C. Kemp Evaluating computational models of preference learning S. Huang, J. Li, J. Ye, T. Wu, K. Chen, A. Fleisher, E. Reiman Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis Z. Wang, G. Schalk, Q. Ji Decoding of Finger Flexion from Electrocorticographic Signals Using Switching Non-Parametric Dynamic Systems M. Park, G. Horwitz, J. Pillow Active learning of neural response functions with Gaussian processes
11:10 - 11:30am	Oral Session 2Session Chair:Michael Collins
	G. Van den Broeck On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference
11:30am - 12:00pm	Coffee Break
12:00 - 12:40pm	Oral Session 3Session Chair:Amir Globerson
	Y. Teh, C. Blundell, L. Elliott Modelling Genetic Variations using Fragmentation-Coagulation Processes A. Saeedi, A. Bouchard-Côté Priors over Recurrent Continuous Time Processes
12:40 - 1:10pm	Spotlight Session 3Session Chair:Amir Globerson
	D. Reichert, P. Series, A. Storkey Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability J. Brea, W. Senn, J. Pfister Sequence learning with hidden units in spiking neural networks K. Rahnama Rad, L. Paninski Information Rates and Optimal Decoding in Large Neural Populations C. Ekanadham, D. Tranchina, E. Simoncelli A blind sparse deconvolution method for neural spike identification S. Ermon, C. Gomes, A. Sabharwal, B. Selman Accelerated Adaptive Markov Chain for Partition Function Computation L. Ren, Y. Wang, D. Dunson, L. Carin The Kernel Beta Process D. Maua, C. de Campos Solving Decision Problems with Limited Information
1:10 - 1:30pm	Oral Session 4Session Chair:Shai Shalev-Shwartz
	D. Mcallester, J. Keshet Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss
1:30 - 2:00pm	Spotlight Session 4Session Chair:Shai Shalev-Shwartz
	R. Servedio, P. Long Algorithms and hardness results for parallel large margin learning F. Bach, E. Moulines Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning A. Guillory, J. Bilmes Online Submodular Set Cover, Ranking, and Repeated Active Learning D. Wipf Sparse Estimation with Structured Dictionaries Y. Liu Universal low-rank matrix recovery from Pauli measurements F. Vitale, N. Cesa-Bianchi, C. Gentile, G. Zappella See the Tree Through the Lines: The Shazoo Algorithm S. Clémençon On U-processes and clustering performance
2:00 - 4:00pm	Break
4:00 - 5:30pm	Oral Session 5Session Chair:Pradeep Ravikumar
	Anna Gilbert Sparsity: algorithms, approximations, and analysis Z. Xiang, H. Xu, P. Ramadge Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries P. Loh, M. Wainwright High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity
5:30 - 5:45pm	Coffee Break
5:45 - 11:59pm	Demonstrations
	C. Stahlhut, A. Stopczynski, J. Larsen, M. Petersen, L. Hansen A smartphone 3D functional brain scanner J. Feng, M. Rasi, A. Ng, Q. Le, M. Quigley, J. Chen, T. Low, W. Zou Haptic Belt with Pedestrian Detection M. Schwartz Reproducing biologically realistic firing patterns on a highly-accelerated neuromorphic hardware system R. Collobert SENNA Natural Language Processing Demo
5:45 - 11:59pm	Poster Session
	O. Kroemer, J. Peters T001 A Non-Parametric Approach to Dynamic Programming J. Pajarinen, J. Peltonen T002 Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning A. Lazaric, M. Restelli T003 Transfer from Multiple MDPs J. Veness, M. Lanctot, M. Bowling T004 Variance Reduction in Monte-Carlo Tree Search A. Farahmand T005 Action-Gap Phenomenon in Reinforcement Learning J. Kolter T006 The Fixed Points of Off-Policy TD D. Lizotte T007 Convergent Fitted Value Iteration with Linear Function Approximation T. Walsh, D. Hewlett, C. Morrison T008 Blending Autonomous Exploration and Apprenticeship Learning O. Maillard, R. Munos, D. Ryabko T009 Selecting the State-Representation in Reinforcement Learning M. Keramati, B. Gutkin T010 A Reinforcement Learning Theory for Homeostatic Regulation D. Simon, N. Daw T011 Environmental statistics and the trade-off between model-based and TD learning in humans G. Konidaris, S. Niekum, P. Thomas T012 TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning A. Jern, C. Lucas, C. Kemp T013 Evaluating computational models of preference learning C. Kemp T014 Inductive reasoning about chimeric creatures B. Chen, V. Navalpakkam, P. Perona T015 Predicting response time and error rates in visual search S. Huang, J. Li, J. Ye, T. Wu, K. Chen, A. Fleisher, E. Reiman T016 Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis Z. Wang, G. Schalk, Q. Ji T017 Decoding of Finger Flexion from Electrocorticographic Signals Using Switching Non-Parametric Dynamic Systems D. Reichert, P. Series, A. Storkey T018 Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability J. Brea, W. Senn, J. Pfister T019 Sequence learning with hidden units in spiking neural networks M. Park, G. Horwitz, J. Pillow T020 Active learning of neural response functions with Gaussian processes P. Gehler, C. Rother, M. Kiefel, L. Zhang, B. Schölkopf T021 Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance B. Lin, C. Zhang, X. He T022 Semi-supervised Regression via Parallel Field Regularization C. Vondrick, D. Ramanan T023 Video Annotation and Tracking with Active Learning A. Yao, J. Gall, L. Gool, R. Urtasun T024 Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Y. Zhao, S. Zhu T025 Image Parsing with Stochastic Scene Grammar X. Wang, X. Bai, X. Yang, W. Liu, L. Latecki T026 Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning N. Morioka, S. Satoh T027 Generalized Lasso based Approximation of Sparse Coding for Visual Recognition H. Koppula, A. Anand, T. Joachims, A. Saxena T028 Semantic Labeling of 3D Point Clouds for Indoor Scenes M. Mozer, B. Link, H. Pashler T029 An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments Z. Li, H. Ning, L. Cao, T. Zhang, Y. Gong, T. Huang T030 Learning to Search Efficiently in High Dimensions H. Le, Z. Bar-Joseph T031 Inferring Interaction Networks using the IBP applied to microRNA Target Prediction C. Yu, R. Greiner, H. Lin, V. Baracos T032 Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors J. Bogojeska T033 History distribution matching method for predicting effectiveness of HIV combination therapies O. Chapelle, L. Li T034 An Empirical Evaluation of Thompson Sampling P. Li, A. Shrivastava, J. Moore, A. König T035 Hashing Algorithms for Large-Scale Learning Z. Xiang, H. Xu, P. Ramadge T036 Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries M. Yamada, T. Suzuki, T. Kanamori, H. Hachiya, M. Sugiyama T037 Relative Density-Ratio Estimation for Robust Distribution Comparison C. Archambeau, S. Guo, O. Zoeter T038 Sparse Bayesian Multi-Task Learning P. Loh, M. Wainwright T039 High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Z. Zhang, L. Ladicky, P. Torr, A. Saffari T040 Learning Anchor Planes for Classification S. Shalev-Shwartz, Y. Wexler, A. Shashua T041 ShareBoost: Efficient multiclass learning with feature sharing Q. Sun, R. Chattopadhyay, S. Panchanathan, J. Ye T042 A Two-Stage Weighting Framework for Multi-Source Domain Adaptation M. Kloft, G. Blanchard T043 The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning C. Lampert T044 Maximum Margin Multi-Label Structured Prediction D. Mcallester, J. Keshet T045 Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss A. Carpentier, O. Maillard, R. Munos T046 Sparse Recovery with Brownian Sensing C. Boutsidis, P. Drineas, M. Magdon-Ismail T047 Sparse Features for PCA-Like Linear Regression A. Tewari, P. Ravikumar, I. Dhillon T049 Greedy Algorithms for Structurally Constrained High Dimensional Problems E. Grave, G. Obozinski, F. Bach T050 Trace Lasso: a trace norm regularization for correlated designs N. Nguyen, N. Nasrabadi, T. Tran T051 Robust Lasso with missing and grossly corrupted observations D. Wipf T052 Sparse Estimation with Structured Dictionaries B. Shaw, B. Huang, T. Jebara T053 Learning a Distance Metric from a Network W. Wang, M. Carreira-Perpinan, Z. Lu T054 A Denoising View of Matrix Completion R. Gomes, P. Welinder, A. Krause, P. Perona T055 Crowdclustering W. Brendel, R. Romo, C. Machens T056 Demixed Principal Component Analysis O. Dikmen, C. Févotte T057 Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation I. Takeuchi, M. Sugiyama T058 Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Y. Zhang, Z. Lu T059 Penalty Decomposition Methods for Rank Minimization L. Boyles, A. Korattikara, D. Ramanan, M. Welling T060 Statistical Tests for Optimization Efficiency Y. Kawahara, T. Washio T061 Prismatic Algorithm for Discrete D.C. Programming Problem C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar T062 Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation S. Wiesler, H. Ney T063 A Convergence Analysis of Log-Linear Training F. Bach, E. Moulines T064 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning A. Cotter, O. Shamir, N. Srebro, K. Sridharan T065 Better Mini-Batch Algorithms via Accelerated Gradient Methods Y. Seldin, P. Auer, F. Laviolette, J. Shawe-Taylor, R. Ortner T066 PAC-Bayesian Analysis of Contextual Bandits A. Anandkumar, K. Chaudhuri, D. Hsu, S. Kakade, L. Song, T. Zhang T067 Spectral Methods for Learning Multivariate Latent Tree Structure R. Servedio, P. Long T068 Algorithms and hardness results for parallel large margin learning E. Vernet, R. Williamson, M. Reid T069 Composite Multiclass Losses N. Mehta, P. Tadepalli, A. Fern T070 Autonomous Learning of Action Models for Planning Y. Liu T071 Universal low-rank matrix recovery from Pauli measurements M. Lopes, L. Jacob, M. Wainwright T072 A More Powerful Two-Sample Test in High Dimensions using Random Projection M. Kapralov, R. Panigrahy T073 Prediction strategies without loss B. Sriperumbudur, K. Fukumizu, G. Lanckriet T074 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint R. Gibson, D. Szafron T075 On Strategy Stitching in Large Extensive Form Multiplayer Games V. Gabillon, M. Ghavamzadeh, A. Lazaric, S. Bubeck T076 Multi-Bandit Best Arm Identification Y. Yue, C. Guestrin T077 Linear Submodular Bandits and their Application to Diversified Retrieval T. van Erven, P. Grunwald, W. Koolen, S. Rooij T078 Adaptive Hedge N. Srebro, K. Sridharan, A. Tewari T079 On the Universality of Online Mirror Descent A. Guillory, J. Bilmes T080 Online Submodular Set Cover, Ranking, and Repeated Active Learning A. Carpentier, R. Munos T081 Finite Time Analysis of Stratified Sampling for Monte Carlo F. Vitale, N. Cesa-Bianchi, C. Gentile, G. Zappella T082 See the Tree Through the Lines: The Shazoo Algorithm G. Blanchard, G. Lee, C. Scott T083 Generalizing from Several Related Classification Tasks to a New Unlabeled Sample S. Clémençon T084 On U-processes and clustering performance K. Rahnama Rad, L. Paninski T085 Information Rates and Optimal Decoding in Large Neural Populations Y. Qi, F. Yan T086 EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning X. Pitkow, Y. Ahmadian, K. Miller T087 Learning unbelievable marginal probabilities C. Ekanadham, D. Tranchina, E. Simoncelli T088 A blind sparse deconvolution method for neural spike identification S. Ermon, C. Gomes, A. Sabharwal, B. Selman T089 Accelerated Adaptive Markov Chain for Partition Function Computation J. Jiang, P. Rai, H. Daume III T090 Message-Passing for Approximate MAP Inference with Latent Variables A. Saeedi, A. Bouchard-Côté T091 Priors over Recurrent Continuous Time Processes Y. Teh, C. Blundell, L. Elliott T092 Modelling Genetic Variations using Fragmentation-Coagulation Processes A. Damianou, M. Titsias, N. Lawrence T093 Variational Gaussian Process Dynamical Systems D. Kim, E. Sudderth T094 The Doubly Correlated Nonparametric Topic Model L. Ren, Y. Wang, D. Dunson, L. Carin T095 The Kernel Beta Process K. Dembczynski, W. Waegeman, W. Cheng, E. Hullermeier T096 An Exact Algorithm for F-Measure Maximization A. Krause, C. Ong T097 Contextual Gaussian Process Bandit Optimization O. Khan, P. Poupart, J. Agosta T098 Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints D. Maua, C. de Campos T099 Solving Decision Problems with Limited Information S. Ding, G. Wahba, X. Zhu T100 Learning Higher-Order Graph Structure with Features by Structure Penalty G. Van den Broeck T101 On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference F. Stimberg, M. Opper, G. Sanguinetti, A. Ruttor T102 Inference in continuous time changepoint point models A. Allahverdyan, A. Galstyan T103 Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs S. Yang, A. Rahimi T104 Structure Learning for Optimization
Wednesday, December 14
8:00am - 5:30pm	Registration Desk
9:30 - 10:40am	Oral Session 6Session Chair:Raquel Urtasun
	B. Schölkopf From kernels to causal inference A. Anandkumar, V. Tan, A. Willsky High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
10:40 - 11:10am	Spotlight Session 5Session Chair:Raquel Urtasun
	M. Kolar, S. Balakrishnan, A. Rinaldo, A. Singh Minimax Localization of Structural Information in Large Noisy Matrices A. Jalali, C. Johnson, P. Ravikumar On Learning Discrete Graphical Models using Greedy Methods R. Salakhutdinov, J. Tenenbaum, A. Torralba Learning to Learn with Compound HD Models A. Ion, J. Carreira, C. Sminchisescu Probabilistic Joint Image Segmentation and Labeling R. Girshick, P. Felzenszwalb, D. Mcallester Object Detection with Grammar Models I. Kokkinos Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound V. Ordonez, G. Kulkarni, T. Berg Im2Text: Describing Images Using 1 Million Captioned Photographs
11:10 - 11:30am	Oral Session 7Session Chair:Katherine Heller
	P. Krähenbühl, V. Koltun Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
11:30am - 12:00pm	Coffee Break
12:00 - 12:40pm	Oral Session 8Session Chair:Phil Long
	N. Cesa-Bianchi, O. Shamir Efficient Online Learning via Randomized Rounding M. Schmidt, N. Le Roux, F. Bach Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
12:40 - 1:10pm	Spotlight Session 6Session Chair:Phil Long
	Y. Abbasi-yadkori, D. Pal, C. Szepesvari Improved Algorithms for Linear Stochastic Bandits S. Mannor, O. Shamir From Bandits to Experts: On the Value of Side-Observations M. Raginsky, A. Rakhlin Lower Bounds for Passive and Active Learning T. Gao, D. Koller Active Classification based on Value of Classifier J. Azimi, A. Fern, X. Fern Budgeted Optimization with Concurrent Stochastic-Duration Experiments J. Liu, L. Sun, J. Ye Projection onto A Nonnegative Max-Heap M. Alamgir, U. von Luxburg Phase transition in the family of p-resistances
1:10 - 1:30pm	Oral Session 9Session Chair:Cedric Archambeau
	S. Kpotufe k-NN Regression Adapts to Local Intrinsic Dimension
1:30 - 2:00pm	Spotlight Session 7Session Chair:Cedric Archambeau
	D. Sontag, D. Roy Complexity of Inference in Latent Dirichlet Allocation A. Graves Practical Variational Inference for Neural Networks Q. Zhao, C. Caiafa, D. Mandic, L. Zhang, T. Ball, A. Schulze-bonhage, A. CICHOCKI A Multilinear Subspace Regression Method Using Orthogonal Tensors Decompositions J. Ngiam, P. Koh, Z. Chen, S. Bhaskar, A. Ng Sparse Filtering D. Perrault-Joncas, M. Meila Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators S. Balakrishnan, M. Xu, A. Krishnamurthy, A. Singh Noise Thresholds for Spectral Clustering
2:00 - 4:00pm	Break
4:00 - 5:30pm	Oral Session 10Session Chair:Joelle Pineau
	Bernard Chazelle Natural Algorithms D. Karger, S. Oh, D. Shah Iterative Learning for Reliable Crowdsourcing Systems J. Abernethy, R. Frongillo A Collaborative Mechanism for Crowdsourcing Prediction Problems
5:30 - 5:45pm	Coffee Break
5:45 - 11:59pm	Demonstrations
	D. García Sánchez, D. Insua AISoy1, a Robot that Perceives, Feels and Makes Decisions R. Zhou, L. Zhang Contour-Based Large Scale Image Retrieval Platform M. Faulkner Detecting Quakes with Clouds and Phones: the Community Seismic Network M. Braun, M. Jugel, K. Müller Real-time social media analysis with TWIMPACT
5:45 - 11:59pm	Poster Session
	M. Pacer, T. Griffiths W001 A rational model of causal inference with continuous causes J. Abbott, K. Heller, Z. Ghahramani, T. Griffiths W002 Testing a Bayesian Measure of Representativeness Using a Large Image Database F. Khan, X. Zhu, B. Mutlu W003 How Do Humans Teach: On Curriculum Learning and Teaching Dimension E. Orhan, R. Jacobs W004 Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations P. Isola, D. Parikh, A. Torralba, A. Oliva W005 Understanding the Intrinsic Memorability of Images J. Austerweil, A. Friesen, T. Griffiths W006 An ideal observer model for identifying the reference frame of objects B. Chen, D. Carlson, L. Carin W007 On the Analysis of Multi-Channel Neural Spike Data J. Shelton, J. Bornschein, A. Sheikh, P. Berkes, J. Lucke W008 Select and Sample:— A Model of Efficient Neural Inference and Learning A. Fletcher, S. Rangan, L. Varshney, A. Bhargava W009 Neural Reconstruction with Approximate Message Passing (NeuRAMP) C. Savin, P. Dayan, M. Lengyel W010 Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories y. karklin, E. Simoncelli W011 Efficient coding with a population of Linear-Nonlinear neurons D. Rezende, D. Wierstra, W. Gerstner W012 Variational Learning for Recurrent Spiking Networks J. Macke, L. Buesing, J. Cunningham, B. Yu, K. Shenoy, M. Sahani W013 Empirical models of spiking in neural populations P. Krähenbühl, V. Koltun W014 Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials J. Deng, S. Satheesh, A. Berg, F. Li W015 Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition A. Ion, J. Carreira, C. Sminchisescu W016 Probabilistic Joint Image Segmentation and Labeling Y. Jia, T. Darrell W017 Heavy-tailed Distances for Gradient Based Image Descriptors C. Li, A. Saxena, T. Chen W018 $\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding V. Jain, S. Turaga, K. Briggman, M. Helmstaedter, W. Denk, H. Seung W019 Learning to Agglomerate Superpixel Hierarchies K. Wnuk, S. Soatto W020 Multiple Instance Filtering V. Ordonez, G. Kulkarni, T. Berg W021 Im2Text: Describing Images Using 1 Million Captioned Photographs B. Alexe, V. Petrescu, V. Ferrari W022 Exploiting spatial overlap to efficiently compute appearance distances between image windows I. Kokkinos W023 Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound R. Girshick, P. Felzenszwalb, D. Mcallester W024 Object Detection with Grammar Models S. Hwang, K. Grauman, F. Sha W025 Learning a Tree of Metrics with Disjoint Visual Features V. Delaitre, J. Sivic, I. Laptev W026 Learning person-object interactions for action recognition in still images K. Chen, A. Salman W027 Extracting Speaker-Specific Information with a Regularized Siamese Deep Network M. Kayala, P. Baldi W028 A Machine Learning Approach to Predict Chemical Reactions N. Boumal, P. Absil W029 RTRMC: A Riemannian trust-region method for low-rank matrix completion D. Tschopp, S. Diggavi, P. Delgosha, S. Mohajer W030 Randomized Algorithms for Comparison-based Search N. Ailon W031 Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity D. Pidan, R. El-Yaniv W032 Selective Prediction of Financial Trends with Hidden Markov Models T. Gao, D. Koller W033 Active Classification based on Value of Classifier M. Telgarsky W034 The Fast Convergence of Boosting P. Shivaswamy, T. Jebara W035 Variance Penalizing AdaBoost K. Fukumizu, L. Song, A. Gretton W036 Kernel Bayes' Rule D. Zhang, Y. Liu, L. Si, J. Zhang, R. Lawrence W037 Multiple Instance Learning on Structured Data X. Lou, F. Hamprecht W038 Structured Learning for Cell Tracking J. Liu, L. Sun, J. Ye W039 Projection onto A Nonnegative Max-Heap O. Delalleau, Y. Bengio W040 Shallow vs. Deep Sum-Product Networks R. Socher, E. Huang, J. Pennin, A. Ng, C. Manning W041 Unfolding Recursive Autoencoders for Paraphrase Detection A. Graves W042 Practical Variational Inference for Neural Networks Q. Zhao, C. Caiafa, D. Mandic, L. Zhang, T. Ball, A. Schulze-bonhage, A. CICHOCKI W043 A Multilinear Subspace Regression Method Using Orthogonal Tensors Decompositions M. Slawski, M. Hein W044 Sparse recovery by thresholded non-negative least squares a. szlam, K. Gregor, Y. LeCun W045 Structured sparse coding via lateral inhibition L. Yuan, J. Liu, J. Ye W046 Efficient Methods for Overlapping Group Lasso A. Armagan, D. Dunson, M. Clyde W047 Generalized Beta Mixtures of Gaussians E. Elhamifar, R. Vidal W048 Sparse Manifold Clustering and Embedding X. Ge, I. Safa, M. Belkin, Y. Wang W049 Data Skeletonization via Reeb Graphs J. Ngiam, P. Koh, Z. Chen, S. Bhaskar, A. Ng W050 Sparse Filtering Q. Le, A. Karpenko, J. Ngiam, A. Ng W051 ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning J. Azimi, A. Fern, X. Fern W052 Budgeted Optimization with Concurrent Stochastic-Duration Experiments M. Shindler, A. Wong, A. Meyerson W053 Fast and Accurate k-means For Large Datasets D. Feldman, M. Faulkner, A. Krause W054 Scalable Training of Mixture Models via Coresets M. Kolar, S. Balakrishnan, A. Rinaldo, A. Singh W055 Minimax Localization of Structural Information in Large Noisy Matrices S. Balakrishnan, M. Xu, A. Krishnamurthy, A. Singh W056 Noise Thresholds for Spectral Clustering V. Mahadevan, C. Wong, J. Costa Pereira, T. Liu, N. Vasconcelos, L. Saul W057 Maximum Covariance Unfolding : Manifold Learning for Bimodal Data S. Rifai, Y. Dauphin, P. Vincent, Y. Bengio, X. Muller W058 The Manifold Tangent Classifier I. Gkioulekas, T. Zickler W059 Dimensionality Reduction Using the Sparse Linear Model Y. Zhang, L. Ghaoui W060 Large-Scale Sparse Principal Component Analysis with Application to Text Data L. Mackey, A. Talwalkar, M. Jordan W061 Divide-and-Conquer Matrix Factorization Y. Hwang, H. Ahn W062 Convergent Bounds on the Euclidean Distance D. Perrault-Joncas, M. Meila W063 Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators D. Newman, E. Bonilla, W. Buntine W064 Improving Topic Coherence with Regularized Topic Models G. Montufar, J. Rauh, N. Ay W065 Expressive Power and Approximation Errors of Restricted Boltzmann Machines D. Sontag, D. Roy W066 Complexity of Inference in Latent Dirichlet Allocation A. Perotte, F. Wood, N. Elhadad, N. Bartlett W067 Hierarchically Supervised Latent Dirichlet Allocation A. Agarwal, J. Duchi W068 Distributed Delayed Stochastic Optimization M. Larsson, J. Ugander W069 A concave regularization technique for sparse mixture models S. Jegelka, H. Lin, J. Bilmes W070 Fast approximate submodular minimization M. Schmidt, N. Le Roux, F. Bach W071 Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Z. Lin, R. Liu, Z. Su W072 Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation D. Garber, E. Hazan W073 Approximating Semidefinite Programs in Sublinear Time B. Recht, C. Re, S. Wright, F. Niu W074 Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent E. Hazan, T. Koren, N. Srebro W075 Beating SGD: Learning SVMs in Sublinear Time P. Long, R. Servedio W076 Learning large-margin halfspaces with more malicious noise S. Kpotufe W077 k-NN Regression Adapts to Local Intrinsic Dimension J. Abernethy, R. Frongillo W078 A Collaborative Mechanism for Crowdsourcing Prediction Problems A. Slivkins W079 Multi-armed bandits on implicit metric spaces N. Cesa-Bianchi, O. Shamir W080 Efficient Online Learning via Randomized Rounding S. Mannor, O. Shamir W081 From Bandits to Experts: On the Value of Side-Observations Y. Abbasi-yadkori, D. Pal, C. Szepesvari W082 Improved Algorithms for Linear Stochastic Bandits A. Agarwal, D. Foster, D. Hsu, S. Kakade, A. Rakhlin W083 Stochastic convex optimization with bandit feedback O. Missura, T. Gaertner W084 Predicting Dynamic Difficulty M. Raginsky, A. Rakhlin W085 Lower Bounds for Passive and Active Learning M. Eberts, I. Steinwart W086 Optimal learning rates for least squares SVMs using Gaussian kernels M. Alamgir, U. von Luxburg W087 Phase transition in the family of p-resistances I. Park, J. Pillow W088 Bayesian Spike-Triggered Covariance Analysis R. Salakhutdinov, J. Tenenbaum, A. Torralba W089 Learning to Learn with Compound HD Models F. Chierichetti, J. Kleinberg, D. Liben-Nowell W090 Reconstructing Patterns of Information Diffusion from Incomplete Observations D. Vu, A. Asuncion, D. Hunter, P. Smyth W091 Continuous-Time Regression Models for Longitudinal Networks J. Lei W092 Differentially Private M-Estimators F. Wauthier, M. Jordan W093 Bayesian Bias Mitigation for Crowdsourcing N. Ding, S. Vishwanathan, Y. Qi W094 t-divergence Based Approximate Inference M. Wick, A. McCallum W095 Query-Aware MCMC O. Stegle, C. Lippert, J. Mooij, N. Lawrence, K. Borgwardt W096 Learning sparse inverse covariance matrices in the presence of confounders A. McHutchon, C. Rasmussen W097 Gaussian Process Training with Input Noise D. Karger, S. Oh, D. Shah W098 Iterative Learning for Reliable Crowdsourcing Systems A. Anandkumar, V. Tan, A. Willsky W099 High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions A. Jalali, C. Johnson, P. Ravikumar W100 On Learning Discrete Graphical Models using Greedy Methods J. Zhou, J. Chen, J. Ye W101 Clustered Multi-Task Learning Via Alternating Structure Optimization
Thursday, December 15
8:00am - 12:00pm	Registration Desk
9:30 - 10:40am	Oral Session 11Session Chair:Mate Lengyel
	Eörs Szathmáry, Chrisantha Fernando The Neuronal Replicator Hypothesis: Novel Mechanisms for Information Transfer and Search in the Brain D. Vu, A. Asuncion, D. Hunter, P. Smyth Continuous-Time Regression Models for Longitudinal Networks
10:40 - 11:20am	Oral Session 12Session Chair:Sasha Rakhlin
	D. Feldman, M. Faulkner, A. Krause Scalable Training of Mixture Models via Coresets M. Shindler, A. Wong, A. Meyerson Fast and Accurate k-means For Large Datasets
11:20am - 12:00pm	Coffee Break
12:00 - 1:10pm	Oral Session 13Session Chair:Jonathan Pillow
	Gilles Laurent Diversity in an Olfactory Network J. Macke, L. Buesing, J. Cunningham, B. Yu, K. Shenoy, M. Sahani Empirical models of spiking in neural populations
1:10 - 1:50pm	Oral Session 14Session Chair:Ronan Collobert
	S. Rifai, Y. Dauphin, P. Vincent, Y. Bengio, X. Muller The Manifold Tangent Classifier F. Chierichetti, J. Kleinberg, D. Liben-Nowell Reconstructing Patterns of Information Diffusion from Incomplete Observations
3:00pm	3:00 Bus to Sierra Nevada
5:00pm	5:00 Bus to the Alhambra
5:30pm	5:30 Bus to Alhambra
8:15pm	8:15 Bus to Sierra Nevada

Workshops (December 16-17, 2011)

Thursday, December 15
4:30 - 9:30pm	Registration Desk
Friday, December 16
7:00 - 10:30am	Registration Desk
7:30am - 8:00pm	Nando de Freitas, Roman Garnett, Frank Hutter, Michael Osborne Bayesian optimization, experimental design and bandits: Theory and applications
7:30am - 8:00pm	Greg Shakhnarovich, Dhruv Batra, Brian Kulis, Kilian Weinberger Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity
7:30am - 8:00pm	Alekh Agarwal, Sasha Rakhlin Computational Trade-offs in Statistical Learning
7:30am - 8:00pm	Gal Elidan, Zoubin Ghahramani, John Lafferty Copulas in Machine Learning
7:30am - 8:00pm	Michael Hirsch, Sarah Bridle, Bernhard Schölkopf, Phil Marshall, Stefan Harmeling, Mark Girolami Cosmology meets Machine Learning
7:30am - 8:00pm	Tatiana Guy, Miroslav Karny, David Wolpert, Alessandro VILLA, David Rios Insua Decision Making with Multiple Imperfect Decision Makers
7:30am - 8:00pm	Yoshua Bengio, Adam Coates, Yann LeCun, Nicolas Le Roux, Andrew Ng Deep Learning and Unsupervised Feature Learning
7:30am - 8:00pm	Karsten Borgwardt, Oliver Stegle, Shipeng Yu, Glenn Fung, Faisal Farooq, Balaji Krishnapuram From statistical genetics to predictive models in personalized medicine
7:30am - 8:00pm	Raymond Mooney, Trevor Darrell, Kate Saenko Integrating Language and Vision
7:30am - 8:00pm	Yevgeny Seldin, Koby Crammer, Nicolò Cesa-Bianchi, Francois Laviolette, John Shawe-Taylor New Frontiers in Model Order Selection
7:30am - 8:00pm	Suvrit Sra, Stephen Wright, Sebastian Nowozin Optimization for Machine Learning
7:30am - 8:00pm	Robert Williamson, John Langford, Ulrike von Luxburg, Mark Reid, Jennifer Wortman Vaughan Relations between machine learning problems – an approach to unify the field
7:30am - 8:00pm	Ameet Talwalkar, Lester Mackey, Mehryar Mohri, Michael Mahoney, Francis Bach, Mike davies, Remi Gribonval, Guillaume Obozinski Sparse Representation and Low-rank Approximation
7:30am - 8:00pm	Joseph Gonzalez, Sameer Singh, Graham Taylor, James Bergstra, Alice Zheng, Misha Bilenko, Yucheng Low, Yoshua Bengio, Michael Franklin, Carlos Guestrin, Andrew McCallum, Alexander Smola, Michael Jordan, Sugato Basu Big Learning: Algorithms, Systems, and Tools for Learning at Scale
7:30am - 8:00pm	Melissa Carroll, Guillermo Cecchi, Kai-min Chang, Moritz Grosse-Wentrup, James Haxby, Georg Langs, Anna Korhonen, Bjoern Menze, Brian Murphy, Janaina Mourao-Miranda, Vittorio Murino, Francisco Pereira, Irina Rish, Mert Sabuncu, Irina Simanova, Bertrand Thirion Machine Learning and Interpretation in Neuroimaging (MLINI-2011)
8:45 - 9:30am	Coffee Break
10:30am - 4:00pm	Ski or Siesta
3:30 - 8:00pm	Registration Desk
5:45 - 6:30pm	Coffee Break
Saturday, December 17
7:00 - 10:30am	Registration Desk
7:30am - 8:00pm	Winter Mason, Jennifer Wortman Vaughan, Hanna Wallach 2nd Workshop on Computational Social Science and the Wisdom of Crowds
7:30am - 8:00pm	Emily Fox, Ryan Adams Bayesian Nonparametric Methods: Hope or Hype?
7:30am - 8:00pm	Quoc V. Le, Marc'Aurelio Ranzato, Russ Salakhutdinov, Josh Tenenbaum, Andrew Ng Challenges in Learning Hierarchical Models: Transfer Learning and Optimization
7:30am - 8:00pm	Jean-Marc Andreoli, Cedric Archambeau, Guillaume Bouchard, Shengbo Guo, Kristian Kersting, Scott Sanner, Martin Szummer, Paolo Viappiani, Onno Zoeter Choice Models and Preference Learning
7:30am - 8:00pm	Andreas Krause, Pradeep Ravikumar, Stefanie Jegelka, Jeff Bilmes Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback
7:30am - 8:00pm	John Blitzer, Corinna Cortes, Afshin Rostamizadeh Domain Adaptation Workshop: Theory and Application
7:30am - 8:00pm	Antoine Bordes, Jason Weston, Ronan Collobert, Leon Bottou Learning Semantics
7:30am - 8:00pm	Thomas Dietterich, J. Zico Kolter, Matthew Brown Machine Learning for Sustainability
7:30am - 8:00pm	Jean-Philippe Vert, Gunnar Raetsch, Yanjun Qi, Tomer Hertz, Anna Goldenberg, Christina Leslie Machine Learning in Computational Biology
7:30am - 8:00pm	Michael Hirsch, Stefan Harmeling, Rob Fergus, Peyman Milanfar Machine Learning meets Computational Photography
7:30am - 8:00pm	Marcello Pelillo, Joachim Buhmann, Tiberio Caetano, Bernhard Schölkopf, Larry Wasserman Philosophy and Machine Learning
7:30am - 8:00pm	Rafael Ramirez, Darrell Conklin, Douglas Eck, Ryan Rifkin The 4th International Workshop on Music and Machine Learning: Learning from Musical Structure
8:45 - 9:30am	Coffee Break
10:30am - 4:00pm	Ski or Siesta
3:30 - 8:00pm	Registration Desk
5:45 - 6:30pm	Coffee Break
9:00 - 11:00pm	Tapas Reception

The schedule was last modified on 2012-2-2 20:37 PST