Schedule

Tutorials (December 3, 2012)

Sunday, December 2
4:00 - 8:00pm	Registration Desk
Monday, December 3
8:00 - 9:30am	Breakfast
8:00am - 6:00pm	Registration Desk
9:30 - 11:30am	Paul Fearnhead Exact Approximate Learning
9:30 - 11:30am	Lise Getoor Representation, Inference and Learning in Structured Statistical Models
10:00 - 10:30am	Coffee break
1:00 - 3:00pm	Emma Brunskill, Geoffrey J Gordon Machine Learning for Student Learning
1:00 - 3:00pm	Joel A Tropp User-Friendly Tools for Studying Random Matrices
3:00 - 3:30pm	Coffee break
3:30 - 5:30pm	Giulio Tononi Consciousness and Information Theory
3:30 - 5:30pm	James Spall Stochastic Search and Optimization

Conference Sessions (December 3-6, 2012)

Monday, December 3
6:30 - 6:55pm	Opening Remarks and Awards
7:00 - 11:59pm	Poster Session and Reception
	R. Kondor, W. Dempsey M1 Multiresolution analysis on the symmetric group M. Hardt, K. Ligett, F. McSherry M2 A Simple and Practical Algorithm for Differentially Private Data Release O. Arandjelovic M3 Assessing Blinding in Clinical Trials D. Ciresan, A. Giusti, l. Gambardella, J. Schmidhuber M4 Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images C. Mayr, P. Stärke, J. Partzsch, L. Cederstroem, R. Schüffny, Y. Shuai, N. DU, H. Schmidt M5 Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation A. Smerieri, F. Duport, Y. Paquot, M. Haelterman, S. Massar M6 Analog readout for optical reservoir computers C. Niu, S. Nandyala, W. Sohn, T. Sanger M7 Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA J. Jiang, A. Teichert, H. Daume III, J. Eisner M8 Learned Prioritization for Trading Off Accuracy and Speed A. Boularias, O. Kroemer, J. Peters M9 Algorithms for Learning Markov Field Policies Z. Zamani, S. Sanner, P. Poupart, K. Kersting M10 Symbolic Dynamic Programming for Continuous State and Observation POMDPs K. Chen, M. Bowling M11 Tractable Objectives for Robust Policy Optimization M. Ibrahimi, A. Javanmard, B. Van Roy M12 Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems P. Li, A. Owen, C. Zhang M13 One Permutation Hashing W. Kong, W. Li M14 Isotropic Hashing C. Zhang, J. Ye, L. Zhang M15 Generalization Bounds for Domain Adaptation F. Dinuzzo, B. Schölkopf M16 The representer theorem for Hilbert spaces: a necessary and sufficient condition S. Sra M17 Scalable nonconvex inexact proximal splitting R. Iyer, J. Bilmes M19 Submodular Bregman Divergences with Applications T. Zhao, K. Roeder, H. Liu M20 High-dimensional Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit B. Calderhead, M. Sustik M21 Sparse Approximate Manifolds for Differential Geometric MCMC F. Pokorny, C. Ek, H. Kjellström, D. Kragic M22 Persistent Homology for Learning Densities with Bounded Support M. Rey, V. Roth M23 Meta-Gaussian Information Bottleneck S. Lyons, A. Storkey, S. Sarkka M24 The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Z. Zhang, B. Tu M25 Nonconvex Penalization, Levy Processes and Concave Conjugates L. Elliott, Y. Teh M26 Scalable imputation of genetic data with a discrete fragmentation-coagulation process L. Boyles, M. Welling M27 The Time-Marginalized Coalescent Prior for Hierarchical Clustering P. Ortega, T. Genewein, J. Grau-Moya, D. Balduzzi, D. Braun M28 A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function N. Foti, S. Williamson M29 Slice sampling normalized kernel-weighted completely random measure mixture models D. Belanger, A. Passos, S. Riedel, A. McCallum M30 MAP Inference in Chains using Column Generation P. Sollich, S. Ashton M31 Learning curves for multi-task Gaussian process regression J. Snoek, H. Larochelle, R. Adams M32 Practical Bayesian Optimization of Machine Learning Algorithms B. Rolfs, B. Rajaratnam, D. Guillot, A. Maleki, I. Wong M33 Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation C. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee M34 A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation U. Kamilov, S. Rangan, A. Fletcher, M. Unser M35 Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning N. Ruozzi M36 The Bethe Partition Function of Log-supermodular Graphical Models L. Shi M38 Bayesian Probabilistic Co-Subspace Addition A. Dennis, D. Ventura M39 Learning the Architecture of Sum-Product Networks Using Clustering on Variables M. Volkovs, R. Zemel M40 Efficient Sampling for Bipartite Matching Problems M. Fiterau, A. Dubrawski M41 Projection Retrieval for Classification K. Crammer, Y. Mansour M42 Learning Multiple Tasks using Shared Hypotheses A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu M43 Optimal kernel choice for large-scale two-sample tests P. Kar, P. Jain M44 Supervised Learning with Similarity Functions M. Sugiyama, T. Kanamori, T. Suzuki, M. Plessis, S. Liu, I. Takeuchi M45 Density-Difference Estimation V. Jethava, A. Martinsson, C. Bhattacharyya, D. Dubhashi M46 The Lovasz $\theta$ function, SVMs and finding large dense subgraphs A. Flint, M. Blaschko M47 Perceptron Learning of SAT Y. Yu, O. Aslan, D. Schuurmans M48 A Polynomial-time Form of Robust Regression C. Scherrer, A. Tewari, M. Halappanavar, D. Haglin M49 Feature Clustering for Accelerating Parallel Coordinate Descent M. Sinn, B. Chen M50 Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions R. Gibson, M. Lanctot, N. Burch, D. Szafron M51 Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions J. Acharya, H. Das, A. Orlitsky M52 Tight Bounds on Redundancy and Distinguishability of Label-Invariant Distributions H. Liu, J. Lafferty, L. Wasserman M53 Exponential Concentration for Mutual Information Estimation with Application to Forests E. Archer, J. Pillow, I. Park M54 Bayesian estimation of discrete entropy with mixtures of stick-breaking priors A. Birnbaum, S. Shalev-Shwartz M55 Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs S. Wang, Z. Zhang M56 A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound S. Kasiviswanathan, H. Wang, A. Banerjee, P. Melville M57 Online L1-Dictionary Learning with Application to Novel Document Detection K. Sricharan, A. Hero M58 Ensemble weighted kernel estimators for multivariate entropy estimation A. Kalogeratos, A. Likas M59 Dip-means: an incremental clustering method for estimating the number of clusters X. Bresson, T. Laurent, D. Uminsky, J. von Brecht M60 Convergence and Energy Landscape for Cheeger Cut Clustering K. Swersky, D. Tarlow, I. Sutskever, R. Zemel, R. Salakhutdinov, R. Adams M61 Cardinality Restricted Boltzmann Machines V. Karasev, A. Chiuso, S. Soatto M62 Controlled Recognition Bounds for Visual Learning and Exploration N. Li, L. Latecki M63 Clustering Aggregation as Maximum-Weight Independent Set Z. Yang, T. Hao, O. Dikmen, X. Chen, E. Oja M64 Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Y. Gong, S. Kumar, V. Verma, S. Lazebnik M65 Angular Quantization based Binary Codes for Fast Similarity Search K. Chaudhuri, A. Sarwate, K. Sinha M66 Near-optimal Differentially Private Principal Components M. Coudron, G. Lerman M67 On the Sample Complexity of Robust PCA Y. He, Y. Qi, k. kavukcuoglu, H. Park M68 Learning the Dependency Structure of Latent Factors M. Xu, J. Zhu, B. Zhang M69 Bayesian Nonparametric Maximum Margin Matrix Factorization for Collaborative Prediction S. Gerrish, D. Blei M71 How They Vote: Issue-Adjusted Models of Legislative Behavior H. Park, e. Jain, Y. Sheikh M72 3D Gaze Concurrences from Head-mounted Cameras C. Chen, J. Huang M73 Compressive Sensing MRI with Wavelet Tree Sparsity W. Li, N. Vasconcelos M74 Recognizing Activities by Attribute Dynamics Y. Zhou, X. Bai, W. Liu, L. Latecki M75 Fusion with Diffusion for Robust Visual Tracking M. Pachitariu, M. Sahani M76 Learning visual motion in recurrent neural networks F. Lieder, T. Griffiths, N. Goodman M77 Burn-in, bias, and the rationality of anchoring V. Mahadevan, N. Vasconcelos M78 On the connections between saliency and tracking H. Zhuo, Q. Yang, S. Kambhampati M79 Action-Model Based Multi-agent Plan Recognition N. Srivastava, P. Schrater M80 Rational inference of relative preferences J. Shelton, P. Sterne, J. Bornschein, A. Sheikh, J. Lucke M81 Why MCA? Nonlinear Spike-and-slab Sparse Coding for Neurally Plausible Image Encoding U. Maoz, S. Ye, I. Ross, A. Mamelak, C. Koch M82 A System for Predicting Action Content On-Line and in Real Time before Action Onset in Humans – an Intracranial Study K. Gregor, D. Chklovskii M83 A lattice filter model of the visual pathway C. Hinrichs, V. Singh, J. Peng, S. Johnson M84 Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging W. Kim, D. Pachauri, C. Hatt, M. Chung, S. Johnson, V. Singh M85 Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination P. Kindermans, H. Verschore, D. Verstraeten, B. Schrauwen M86 A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling D. Balduzzi, M. Besserve M87 Towards a learning-theoretic analysis of spike-timing dependent plasticity J. Bouvrie, J. Slotine M88 Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes S. Habenschuss, J. Bill, B. Nessler M89 Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints J. Rombouts, S. Bohte, P. Roelfsema M90 Neurally Plausible Reinforcement Learning of Working Memory Tasks R. Cazé, M. Humphries, B. Gutkin M91 Spiking and saturating dendrites differentially expand single neuron computation capacity. S. Koyama M92 Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing X. Wei, A. Stocker M93 Efficient coding connects prior and likelihood function in perceptual Bayesian inference O. Meshi, T. Jaakkola, A. Globerson Tu94 Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
Tuesday, December 4
7:30 - 9:30am	Breakfast Sponsored By Winton Capital
8:00am - 6:00pm	Registration Desk
9:00 - 10:10am	Oral Session 1Session Chair:Raquel Urtasun
	Scott Aaronson Quantum information and the Brain F. Han, H. Liu TCA: High Dimensional Principal Component Analysis for non-Gaussian Data
10:10 - 10:30am	Spotlight Session 1
	W. Koolen, D. Adamskiy, M. Warmuth Putting Bayes to sleep L. Ralaivola Confusion-Based Online Learning and a Passive-Aggressive Scheme K. Crammer, T. Wagner Volume Regularization for Binary Classification C. Zhou, j. Park, Y. Fu Fast Resampling Weighted v-Statistics Y. Wiener, R. El-Yaniv Pointwise Tracking the Optimal Regression Function
10:30 - 11:00am	Coffee Break
11:00 - 11:40am	Oral Session 2Session Chair:Sebastien Bubeck
	B. de Balle Pigem, M. Mohri Spectral Learning of General Weighted Automata via Constrained Matrix Completion A. Rakhlin, O. Shamir, K. Sridharan Relax and Randomize : From Value to Algorithms
11:40am - 12:05pm	Spotlight Session 2
	A. Defazio, T. Caetano A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation C. Sawade, N. Landwehr, T. Scheffer Active Comparison of Prediction Models S. Jun, L. Wang, A. Bouchard-Côté Entangled Monte Carlo C. Blundell, K. Heller, J. Beck Modelling Reciprocating Relationships with Hawkes processes Z. Ghahramani, Y. Zhang, C. Sutton, A. Storkey Continuous Relaxations for Discrete Hamiltonian Monte Carlo P. Vernaza, D. Bagnell Efficient high dimensional maximum entropy modeling via symmetric partition functions
12:05 - 2:00pm	Lunch Break
2:00 - 3:30pm	Oral Session 3Session Chair:Lin Xiao
	Stephane Mallat Classification with Deep Invariant Scattering Networks N. Le Roux, M. Schmidt, F. Bach A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets J. Giesen, J. Mueller, S. Laue, S. Swiercy Approximating Concavely Parameterized Optimization Problems
3:30 - 3:50pm	Spotlight Session 3
	S. Becker, J. Fadili A quasi-Newton proximal splitting method B. Recht, C. Re, J. Tropp, V. Bittorf Factoring nonnegative matrices with linear programs T. Ngo, Y. Saad Scaled Gradients on Grassmann Manifolds for Matrix Completion K. Hsiao, K. Xu, J. Calder, A. Hero Multi-criteria Anomaly Detection using Pareto Depth Analysis D. Lopez-Paz, J. Hernández-Lobato, B. Schölkopf Semi-Supervised Domain Adaptation with Non-Parametric Copulas
3:50 - 4:20pm	Coffee Break
4:20 - 5:40pm	Oral Session 4Session Chair:Gunnar Raetsch
	L. Buesing, J. Macke, M. Sahani Spectral learning of linear dynamics from generalised-linear observations with application to neural population data H. Wang, F. Nie, H. Huang, J. Yan, S. Kim, S. Risacher, A. Saykin, L. Shen High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction N. Srivastava, R. Salakhutdinov Multimodal Learning with Deep Boltzmann Machines R. Gens, P. Domingos Discriminative Learning of Sum-Product Networks
5:40 - 6:00pm	Spotlight Session 4
	A. Krizhevsky, I. Sutskever, G. Hinton ImageNet Classification with Deep Convolutional Neural Networks B. Alexe, N. Heess, Y. Teh, V. Ferrari Searching for objects driven by context S. Fidler, S. Dickinson, R. Urtasun 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model M. Hejrati, D. Ramanan Analyzing 3D Objects in Cluttered Images X. Ren, L. Bo Discriminatively Trained Sparse Code Gradients for Contour Detection
7:00 - 11:59pm	Demonstrations
	A. Ghoreyshi, T. Sanger, J. Rocamora A Stochastic Spiking Network Model of Sensorimotor Control J. Deng, J. Krause, Z. Huang, A. Berg, F. Li EVA: Engine for Visual Annotation M. Gabel, E. Renshaw, A. Schuster, R. Gilad - Bachrach Gait analysis using the Kinect sensor I. Guyon Gesture recognition with Kinect L. Jayet Bray, D. Tanna, F. Harris, Jr NCS: A Large-Scale Brain Simulator T. Boult Real-time Fusion/normalization of Multiple SVM with libMR J. LeBoeuf Ubiquitous Content: How musicians will search for every riff, musical phrase, and idea ever recorded.
7:00 - 11:59pm	Poster Session
	N. Dalvi, A. Parameswaran, V. Rastogi Tu1 Minimizing Uncertainty in Pipelines S. Cohen, M. Collins Tu2 Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Z. Chen, K. Zhang, L. CHAN Tu3 Causal discovery with scale-mixture model for spatiotemporal variance dependencies E. Talvitie Tu4 Learning Partially Observable Models Using Temporally Abstract Decision Trees A. Barreto, D. Precup, J. Pineau Tu5 On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization N. Srivastava, R. Salakhutdinov Tu6 Multimodal Learning with Deep Boltzmann Machines R. Salakhutdinov, G. Hinton Tu7 A Better Way to Pre-Train Deep Boltzmann Machines A. Coates, A. Karpathy, A. Ng Tu8 Emergence of Object-Selective Features in Unsupervised Feature Learning R. Kiros, C. Szepesvari Tu9 Deep Representations and Codes for Image Auto-Annotation F. Han, H. Liu Tu10 High Dimensional Semiparametric Scale-invariant Principal Component Analysis D. Lopez-Paz, J. Hernández-Lobato, B. Schölkopf Tu11 Semi-Supervised Domain Adaptation with Non-Parametric Copulas J. Duchi, M. Jordan, M. Wainwright, A. Wibisono Tu12 Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods T. Ngo, Y. Saad Tu13 Scaled Gradients on Grassmann Manifolds for Matrix Completion A. Delong, O. Veksler, A. Osokin, Y. Boykov Tu14 Minimizing Sparse High-Order Energies by Submodular Vertex-Cover X. Zhang, Y. Yu, D. Schuurmans Tu15 Accelerated Training for Matrix-norm Regularization: A Boosting Approach J. Giesen, J. Mueller, S. Laue, S. Swiercy Tu16 Approximating Concavely Parameterized Optimization Problems E. Elhamifar, G. Sapiro, R. Vidal Tu17 Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery M. White, Y. Yu, X. Zhang, D. Schuurmans Tu18 Convex Multi-view Subspace Learning E. Hazan, Z. Karnin Tu19 A Polylog Pivot Steps Simplex Algorithm for Classification A. Agarwal, S. Negahban, M. Wainwright Tu20 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions M. Mahdavi, T. Yang, R. Jin, S. Zhu Tu21 Stochastic Gradient Descent with Only One Projection X. Chen, Q. Lin, J. Pena Tu22 Optimal Regularized Dual Averaging Methods for Stochastic Optimization N. Le Roux, M. Schmidt, F. Bach Tu23 A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets D. Venugopal, V. Gogate Tu24 On Lifting the Gibbs Sampling Algorithm C. Sawade, N. Landwehr, T. Scheffer Tu25 Active Comparison of Prediction Models S. Feldman, M. Gupta, B. Frigyik Tu26 Multi-Task Averaging J. Weiss, S. Natarajan, D. Page Tu27 Multiplicative Forests for Continuous-Time Processes S. Jun, L. Wang, A. Bouchard-Côté Tu28 Entangled Monte Carlo M. Khan, S. Mohamed, K. Murphy Tu29 Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression A. Schwing, T. Hazan, M. Pollefeys, R. Urtasun Tu30 Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins J. Hensman, M. Rattray, N. Lawrence Tu31 Fast Variational Inference in the Conjugate Exponential Family J. Pacheco, E. Sudderth Tu32 Minimization of Continuous Bethe Approximations: A Positive Variation S. Nakajima, R. Tomioka, M. Sugiyama, S. Babacan Tu33 Perfect Dimensionality Recovery by Variational Bayesian PCA M. Deisenroth, S. Mohamed Tu34 Expectation Propagation in Gaussian Process Dynamical Systems J. Lloyd, D. Roy, P. Orbanz, Z. Ghahramani Tu35 Random function priors for exchangeable graphs and arrays K. Wakabayashi, T. Miura Tu36 Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models M. Osborne, D. Duvenaud, R. Garnett, C. Rasmussen, S. Roberts, Z. Ghahramani Tu37 Active Learning of Model Evidence Using Bayesian Quadrature H. Park, J. Kim, S. Park, S. Yun, C. Yoo Tu38 Phoneme Classification using Constrained Variational Gaussian Process Dynamical System S. Ermon, C. Gomes, A. Sabharwal, B. Selman Tu39 Density Propagation and Improved Bounds on the Partition Function H. Liu, F. Han Tu40 High Dimensional Transelliptical Graphical Models Z. Ghahramani, Y. Zhang, C. Sutton, A. Storkey Tu42 Continuous Relaxations for Discrete Hamiltonian Monte Carlo C. Zhang, T. Sun Tu43 Calibrated Elastic Regularization in Matrix Completion A. Anandkumar, R. Valluvan Tu44 Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs T. Papai, H. Kautz, D. Stefankovic Tu45 Slice Normalized Dynamic Markov Logic Networks K. Crammer, T. Wagner Tu46 Volume Regularization for Binary Classification B. de Balle Pigem, M. Mohri Tu47 Spectral Learning of General Weighted Automata via Constrained Matrix Completion G. Canas, L. Rosasco Tu48 Learning Probability Measures with respect to Optimal Transport Metrics C. Zhou, j. Park, Y. Fu Tu49 Fast Resampling Weighted v-Statistics A. Greenwald, J. Li, E. Sodomka Tu50 Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand N. Della Penna, M. Reid, R. Frongillo Tu51 Interpreting prediction markets: a stochastic approach H. Tyagi, V. Cevher Tu52 Active Learning of Multi-Index Function Models O. Maillard Tu53 Hierarchical Optimistic Region Selection driven by Curiosity A. Sani, A. Lazaric, R. Munos Tu54 Risk-Aversion in Multi-armed Bandits A. Carpentier, O. Maillard Tu55 Online allocation and homogeneous partitioning for piecewise constant mean-approximation A. Carpentier, R. Munos Tu56 Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions W. Koolen, D. Adamskiy, M. Warmuth Tu57 Putting Bayes to sleep M. Herbster, F. Vitale, S. Pasteris Tu58 Online Sum-Product Computation Z. Wang, S. Lyu, G. Schalk, Q. Ji Tu59 Learning with Target Prior A. Glazer, M. Lindenbaoum, S. Markovitch Tu60 Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data X. Wu, Z. Li, S. Chang, J. Wright, A. So Tu61 Learning with Partially Absorbing Random Walks K. Jiang, B. Kulis, M. Jordan Tu62 Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models F. PETRALIA, V. Rao, D. Dunson Tu63 Repulsive Mixtures L. Theis, J. Sohl-Dickstein, M. Bethge Tu64 Training sparse natural image models with a fast Gibbs sampler of an extended state space S. Arora, R. Ge, A. Moitra, S. Sachdeva Tu65 Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders F. Han, H. Liu Tu66 TCA: High Dimensional Principal Component Analysis for non-Gaussian Data R. Foygel, N. Srebro, R. Salakhutdinov Tu67 Matrix reconstruction with the local max norm K. Hsiao, K. Xu, J. Calder, A. Hero Tu68 Multi-criteria Anomaly Detection using Pareto Depth Analysis M. Harel, S. Mannor Tu69 The Perturbed Variation S. Hauberg, O. Freifeld, M. Black Tu70 A Geometric take on Metric Learning T. Jansen Hansen, M. Mahoney Tu71 Semi-supervised Eigenvectors for Locally-biased Learning A. Ziegler, E. Christiansen, D. Kriegman, S. Belongie Tu72 LUCID: Locally Uniform Comparison Image Descriptor G. Huang, M. Mattar, H. Lee, E. Learned-Miller Tu73 Learning to Align from Scratch S. Ghosh, E. Sudderth, M. Loper, M. Black Tu74 From Deformations to Parts: Motion-based Segmentation of 3D Objects D. Tran, J. Yuan Tu75 Max-Margin Structured Output Regression for Spatio-Temporal Action Localization S. Yang, L. Bo, J. Wang, L. Shapiro Tu76 Unsupervised template learning for fine-grained object recognition S. Eslami, C. Williams Tu77 A Generative Model for Parts-based Object Segmentation X. Ren, L. Bo Tu78 Discriminatively Trained Sparse Code Gradients for Contour Detection M. Hejrati, D. Ramanan Tu79 Analyzing 3D Objects in Cluttered Images S. Karayev, T. Baumgartner, M. Fritz, T. Darrell Tu80 Timely Object Recognition S. Hwang, K. Grauman, F. Sha Tu81 Semantic Kernel Forests from Multiple Taxonomies S. Fidler, S. Dickinson, R. Urtasun Tu82 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model J. Xiao, B. Russell, A. Torralba Tu83 Localizing 3D cuboids in single-view images W. Yang, Y. Wang, A. Vahdat, G. Mori Tu84 Kernel Latent SVM for Visual Recognition T. Harada Tu85 Graphical Gaussian Vector for Image Categorization E. Mezuman, Y. Weiss Tu86 Learning about Canonical Views from Internet Image Collections Y. Noh, F. Park, D. Lee Tu87 Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification A. Khosla, J. Xiao, A. Torralba, A. Oliva Tu88 Modeling the Forgetting Process using Image Regions F. Cao, S. Ray Tu89 Bayesian Hierarchical Reinforcement Learning L. Buesing, J. Macke, M. Sahani Tu90 Spectral learning of linear dynamics from generalised-linear observations with application to neural population data F. Pereira, M. Botvinick Tu91 A systematic approach to extracting semantic information from functional MRI data J. Pillow, J. Scott Tu92 Fully Bayesian inference for neural models with negative-binomial spiking M. Park, J. Pillow Tu93 Bayesian active learning with localized priors for fast receptive field characterization S. Bach, M. Broecheler, L. Getoor, D. O'Leary Tu41 Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
Wednesday, December 5
7:30 - 9:30am	Breakfast
8:00am - 6:00pm	Registration Desk
9:00 - 10:10am	Oral Session 5Session Chair:Fei Sha
	T. Dietterich Challenges for Machine Learning in Computational Sustainability A. Shukla, A. Billard Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics
10:10 - 10:30am	Spotlight Session 5
	T. Jebara, A. Choromanska Majorization for CRFs and Latent Likelihoods A. Lorbert, P. Ramadge Kernel Hyperalignment N. DU, L. Song, A. Smola, M. Yuan Learning Networks of Heterogeneous Influence K. Muandet, K. Fukumizu, F. Dinuzzo, B. Schölkopf Learning from Distributions via Support Measure Machines A. Daniely, S. Sabato, S. Shalev-Shwartz Multiclass Learning Approaches: A Theoretical Comparison with Implications
10:30 - 11:00am	Coffee Break
11:00 - 11:40am	Oral Session 6Session Chair:Csaba Szepesvari
	B. Scherrer, B. Lesner On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes T. Furmston, D. Barber A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
11:40am - 12:05pm	Spotlight Session 6
	T. Moldovan, P. Abbeel Near Optimal Chernoff Bounds for Markov Decision Processes B. Liu, S. Mahadevan, J. Liu Regularized Off-Policy TD-Learning J. Abbott, J. Austerweil, T. Griffiths Human memory search as a random walk in a semantic network A. Khaleghi, D. Ryabko Locating Changes in Highly Dependent Data with Unknown Number of Change Points H. Guruprasad, S. Agarwal Classification Calibration Dimension for General Multiclass Losses P. Gong, J. Ye, C. Zhang Multi-Stage Multi-Task Feature Learning
12:05 - 2:00pm	Lunch Break
2:00 - 3:30pm	Oral Session 7Session Chair:Katherine A Heller
	Stanislas Dehaene Signatures of Conscious Processing in the Human Brain J. Duchi, M. Jordan, M. Wainwright Privacy Aware Learning C. Calauzènes, N. Usunier, P. Gallinari On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking
3:30 - 3:50pm	Spotlight Session 7
	Y. Lan, J. Guo, X. Cheng, T. Liu Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space S. Negahban, S. Oh, D. Shah Iterative ranking from pair-wise comparisons y. karklin, C. Ekanadham, E. Simoncelli Hierarchical spike coding of sound J. Wiens, J. Guttag, E. Horvitz Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task C. Gentile, F. Orabona On Multilabel Classification and Ranking with Partial Feedback
3:50 - 4:20pm	Coffee Break
4:20 - 5:40pm	Oral Session 8Session Chair:Tiberio Caetano
	E. Yang, P. Ravikumar, G. Allen, z. Liu Graphical Models via Generalized Linear Models P. Loh, M. Wainwright No voodoo here! Learning discrete graphical models via inverse covariance estimation A. Kulesza, J. Gillenwater, B. Taskar Near-Optimal MAP Inference for Determinantal Point Processes F. Caron Bayesian nonparametric models for bipartite graphs
5:40 - 6:00pm	Spotlight Session 8
	P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, D. Blei Scalable Inference of Overlapping Communities F. Ruiz, I. Valera, C. Blanco, F. Perez-Cruz Bayesian Nonparametric Modeling of Suicide Attempts M. Zhou, L. Carin Augment-and-Conquer Negative Binomial Processes K. Fukumasu, K. Eguchi, E. Xing Symmetric Correspondence Topic Models for Multilingual Text Analysis A. Anandkumar, D. Foster, D. Hsu, S. Kakade, Y. Liu A Spectral Algorithm for Latent Dirichlet Allocation
7:00 - 11:59pm	Demonstrations
	P. Kindermans, H. Verschore, D. Verstraeten, B. Schrauwen A Fast Accurate Training-less P300 Speller: Unsupervised Learning Uncovers new Possibilities J. Saxe, D. Mentis, C. Greamo Cynomix: A Machine Learning Aided Workbench for Rapid Comprehension of Large Malware Corpora R. Herbrich DIRTBIS - Distributed Real-Time Bayesian Inference Service Y. Low, H. Gu, C. Guestrin GraphLab: A Framework For Machine Learning in the Cloud S. Hershey, B. Vigoda Hardware Accelerated Belief Propagation J. Montgomery, M. Reid Protocols and Structures for Inference: A RESTful API for Machine Learning Services M. Szummer, M. Henderson, C. Breslin, M. Gasic, D. Kim, B. Thomson, P. Tsiakoulis, S. Young The BUDS POMDP Spoken Dialogue System
7:00 - 11:59pm	Poster Session
	J. Ji, J. Li, s. yan, B. Zhang, Q. Tian W1 Super-Bit Locality-Sensitive Hashing G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, A. von Lilienfeld, K. Müller W2 Learning Invariant Representations of Molecules for Atomization Energy Prediction A. Ba, M. Sinn, Y. Goude, P. Pompey W3 Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting J. Lee, M. Sun, S. Kim, G. Lebanon W4 Automatic Feature Induction for Stagewise Collaborative Filtering Y. Zhen, D. Yeung W5 Co-Regularized Hashing for Multimodal Data W. Cheng, E. Huellermeier, W. Waegeman, V. Welker W6 Label Ranking with Partial Abstention based on Thresholded Probabilistic Models H. Azari, D. Parkes, L. Xia W7 Random Utility Theory for Social Choice: Theory and Algorithms S. Chaudhuri, B. Raj W8 Unsupervised Structure Discovery for Semantic Analysis of Audio y. karklin, C. Ekanadham, E. Simoncelli W9 Hierarchical spike coding of sound J. Choi, K. Kim W10 Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions T. Moldovan, P. Abbeel W11 Near Optimal Chernoff Bounds for Markov Decision Processes M. Bellemare, J. Veness, M. Bowling W12 Sketch-Based Linear Value Function Approximation T. Furmston, D. Barber W13 A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, A. Ng W14 Large Scale Distributed Deep Networks R. Gens, P. Domingos W15 Discriminative Learning of Sum-Product Networks H. Larochelle, S. Lauly W16 A Neural Autoregressive Topic Model Y. Chen, H. Lin W17 Feature-aware Label Space Dimension Reduction for Multi-label Classification B. Lin, S. Yang, C. Zhang, J. Ye, X. He W18 Multi-task Vector Field Learning N. Mehta, D. Lee, A. Gray W19 Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Y. Zhang, J. Duchi, M. Wainwright W20 Communication-Efficient Algorithms for Statistical Optimization A. Kapoor, R. Viswanathan, P. Jain W21 Multilabel Classification using Bayesian Compressed Sensing V. Rao, Y. Teh W22 MCMC for continuous-time discrete-state systems F. Lindsten, M. Jordan, T. Schön W23 Ancestor Sampling for Particle Gibbs J. Huang, D. Alexander W24 Probabilistic Event Cascades for Alzheimer's disease B. Kirkpatrick, A. Bouchard-Côté W25 Bayesian Pedigree Analysis using Measure Factorization A. Freno, M. Keller, M. Tommasi W26 Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling S. Yoon, V. Pavlovic W27 Distributed Probabilistic Learning for Camera Networks with Missing Data P. Loh, M. Wainwright W28 No voodoo here! Learning discrete graphical models via inverse covariance estimation P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, D. Blei W29 Scalable Inference of Overlapping Communities L. Liu, T. Dietterich W30 Probabilistic Topic Coding for Superset Label Learning M. Zhou, L. Carin W31 Augment-and-Conquer Negative Binomial Processes J. Zou, R. Adams W32 Priors for Diversity in Generative Latent Variable Models C. Lampert W33 Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Q. Liu, J. Peng, A. Ihler W34 Variational Inference for Crowdsourcing A. Kulesza, J. Gillenwater, B. Taskar W35 Near-Optimal MAP Inference for Determinantal Point Processes E. Challis, D. Barber W36 Affine Independent Variational Inference M. Bryant, E. Sudderth W37 Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes M. Hughes, E. Fox, E. Sudderth W38 Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data C. Wang, D. Blei W39 Truncation-free Online Variational Inference for Bayesian Nonparametric Models D. Knowles, K. Palla, Z. Ghahramani W41 A nonparametric variable clustering model F. Caron, Y. Teh W42 Bayesian nonparametric models for ranked data Y. Wang, B. Chaib-draa W43 A Marginalized Particle Gaussian Process Regression M. Fiori, P. Musé, G. Sapiro W45 Topology Constraints in Graphical Models A. Anandkumar, D. Hsu, F. Huang, S. Kakade W46 Learning Mixtures of Tree Graphical Models A. Defazio, T. Caetano W47 A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation E. Yang, P. Ravikumar, G. Allen, z. Liu W48 Graphical Models via Generalized Linear Models R. Jenatton, N. Le Roux, A. Bordes, G. Obozinski W49 A latent factor model for highly multi-relational data Q. Ho, J. Yin, E. Xing W50 On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks P. Vernaza, D. Bagnell W51 Efficient high dimensional maximum entropy modeling via symmetric partition functions P. Rai, A. Kumar, H. Daume III W52 Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression A. Shukla, A. Billard W53 Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics J. Wang, V. Saligrama W54 Local Supervised Learning through Space Partitioning W. Bi, J. Kwok W55 Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification J. Cid-Sueiro W56 Proper losses for learning from partial labels Y. Mroueh, T. Poggio, L. Rosasco, J. Slotine W57 Multiclass Learning with Simplex Coding A. Daniely, S. Sabato, S. Shalev-Shwartz W58 Multiclass Learning Approaches: A Theoretical Comparison with Implications T. Caetano, X. Liu, J. Petterson W59 Learning as MAP Inference in Discrete Graphical Models S. Sra W60 A new metric on the manifold of kernel matrices with application to matrix geometric means N. DU, L. Song, A. Smola, M. Yuan W61 Learning Networks of Heterogeneous Influence A. Lorbert, P. Ramadge W62 Kernel Hyperalignment T. Jebara, A. Choromanska W63 Majorization for CRFs and Latent Likelihoods J. Duchi, M. Jordan, M. Wainwright W64 Privacy Aware Learning Y. Lan, J. Guo, X. Cheng, T. Liu W65 Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space A. Anandkumar, D. Foster, D. Hsu, S. Kakade, Y. Liu W66 A Spectral Algorithm for Latent Dirichlet Allocation V. Kanade, Z. Liu, B. Radunovic W67 Distributed Non-Stochastic Experts A. Khaleghi, D. Ryabko W68 Locating Changes in Highly Dependent Data with Unknown Number of Change Points C. Calauzènes, N. Usunier, P. Gallinari W69 On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking H. Guruprasad, S. Agarwal W70 Classification Calibration Dimension for General Multiclass Losses N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella W71 A Linear Time Active Learning Algorithm for Link Classification A. Rakhlin, O. Shamir, K. Sridharan W72 Relax and Randomize : From Value to Algorithms N. Cesa-Bianchi, P. Gaillard, G. Lugosi, G. Stoltz W73 Mirror Descent Meets Fixed Share (and feels no regret) V. Gabillon, M. Ghavamzadeh, A. Lazaric W74 Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence T. van Erven, P. Grunwald, M. Reid, R. Williamson W75 Mixability in Statistical Learning M. Streeter, B. McMahan W76 No-Regret Algorithms for Unconstrained Online Convex Optimization L. Ralaivola W77 Confusion-Based Online Learning and a Passive-Aggressive Scheme C. Jin, L. Wang W78 Dimensionality Dependent PAC-Bayes Margin Bound E. Coviello, A. Chan, G. Lanckriet W79 The variational hierarchical EM algorithm for clustering hidden Markov models. A. Ahmed, S. Ravi, S. Narayanamurthy, A. Smola W80 FastEx: Fast Clustering with Exponential Families Y. Chen, S. Sanghavi, H. Xu W81 Clustering Sparse Graphs K. Fukumasu, K. Eguchi, E. Xing W82 Symmetric Correspondence Topic Models for Multilingual Text Analysis M. Paul, M. Dredze W83 Factorial LDA: Sparse Multi-Dimensional Text Models A. Eigenstetter, B. Ommer W84 Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity P. Kontschieder, S. Bulò, A. Criminisi, P. Kohli, M. Pelillo, H. Bischof W85 Context-Sensitive Decision Forests for Object Detection J. Abbott, J. Austerweil, T. Griffiths W86 Human memory search as a random walk in a semantic network T. Nguyen, T. Silander, T. Leong W87 Transferring Expectations in Model-based Reinforcement Learning B. Liu, S. Mahadevan, J. Liu W88 Regularized Off-Policy TD-Learning T. Ueno, Y. Kawahara, K. Hayashi, T. Washio W89 Weighted Likelihood Policy Search with Model Selection S. Druckmann, T. Hu, D. Chklovskii W90 A mechanistic model of early sensory processing based on subtracting sparse representations H. Wang, F. Nie, H. Huang, J. Yan, S. Kim, S. Risacher, A. Saykin, L. Shen W91 High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction Z. Wang, A. Stocker, D. Lee W92 Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss C. Blundell, K. Heller, J. Beck W40 Modelling Reciprocating Relationships with Hawkes processes K. Mohan, M. Chung, S. Han, D. Witten, S. Lee, M. Fazel W44 Structured learning of Gaussian graphical models P. Liang, S. Kakade, D. Hsu W93 Identifiability and Unmixing of Latent Parse Trees
Thursday, December 6
7:30 - 9:30am	Breakfast
8:00am - 6:00pm	Registration Desk
9:00 - 10:10am	Oral Session 9Session Chair:Jean-Philippe Vert
	T. Sejnowski Suspicious Coincidences in the Brain P. Shenoy, A. Yu Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making
10:10 - 10:30am	Spotlight Session 9
	J. Beck, K. Heller, A. Pouget Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models f. janoos, W. Li, N. Subrahmanya, I. Morocz, W. Wells Identification of Recurrent Patterns in the Activation of Brain Networks C. Fung, K. Wong, S. Wu Delay Compensation with Dynamical Synapses S. Bohte Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model P. Lena, P. Baldi, K. Nagata Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction
10:30 - 10:50am	Coffee Break
10:50am - 12:00pm	Oral Session 10Session Chair:Alexander J Smola
	Geoffrey E Hinton, George E Dahl Dropout: A simple and effective way to improve neural networks S. Kpotufe, A. Boularias Gradient Weights help Nonparametric Regressors
12:00 - 12:20pm	Spotlight Session 10
	A. Argyriou, R. Foygel, N. Srebro Sparse Prediction with the $k$-Support Norm X. Pitkow Compressive neural representation of sparse, high-dimensional probabilities D. Ba, B. Babadi, P. Purdon, E. Brown Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential ℓ1-Minimization A. Das, A. Dasgupta, R. Kumar Selecting Diverse Features via Spectral Regularization
12:20 - 12:30pm	Closing Remarks
12:30 - 2:00pm	Lunch Break
2:00 - 6:00pm	Poster Session
	J. Fruitet, A. Carpentier, R. Munos, M. Clerc Th1 Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button J. Wiens, J. Guttag, E. Horvitz Th2 Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task A. Mnih, Y. Teh Th3 Learning Label Trees for Probabilistic Modelling of Implicit Feedback M. Volkovs, R. Zemel Th4 Collaborative Ranking With 17 Parameters J. He, H. Tong, Q. Mei, B. Szymanski Th6 GenDeR: A Generic Diversified Ranking Algorithm S. Negahban, S. Oh, D. Shah Th7 Iterative ranking from pair-wise comparisons S. Boyd, C. Cortes, M. Mohri, A. Radovanovic Th8 Accuracy at the Top J. McAuley, J. Leskovec Th9 Learning to Discover Social Circles in Ego Networks Y. Wang, D. Wang Th10 Cocktail Party Processing via Structured Prediction B. Scherrer, B. Lesner Th11 On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes N. Bhat, C. Moallemi, V. Farias Th12 Non-parametric Approximate Dynamic Programming via the Kernel Method H. He, H. Daume III, J. Eisner Th13 Imitation Learning by Coaching E. Klein, M. Geist, B. PIOT, O. Pietquin Th14 Inverse Reinforcement Learning through Structured Classification A. Guez, D. Silver, P. Dayan Th15 Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search A. Wilson, A. Fern, P. Tadepalli Th16 A Bayesian Approach for Policy Learning from Trajectory Preference Queries A. Farahmand, D. Precup Th17 Value Pursuit Iteration R. Ortner, D. Ryabko Th18 Online Regret Bounds for Undiscounted Continuous Reinforcement Learning T. Osogami Th19 Robustness and risk-sensitivity in Markov decision processes F. Trevizan, M. Veloso Th20 Trajectory-Based Short-Sighted Probabilistic Planning D. Kim, K. Kim, P. Poupart Th21 Cost-Sensitive Exploration in Bayesian Reinforcement Learning R. Socher, B. Bath, B. Huval, C. Manning, A. Ng Th22 Recursive Deep Learning on 3D Point Clouds P. Lena, P. Baldi, K. Nagata Th23 Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction J. Xie, L. Xu, E. Chen Th24 Image Denoising and Inpainting with Deep Neural Networks D. Ba, B. Babadi, P. Purdon, E. Brown Th26 Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential ℓ1-Minimization X. Pitkow Th27 Compressive neural representation of sparse, high-dimensional probabilities K. Tang, V. Ramanathan, F. Li, D. Koller Th28 Shifting Weights: Adapting Object Detectors from Image to Video P. Gong, J. Ye, C. Zhang Th29 Multi-Stage Multi-Task Feature Learning B. Recht, C. Re, J. Tropp, V. Bittorf Th30 Factoring nonnegative matrices with linear programs J. Lee, Y. Sun, M. Saunders Th31 Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form K. Tsianos, S. Lawlor, M. Rabbat Th32 Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization M. Pilanci, L. El Ghaoui, V. Chandrasekaran Th33 Recovery of Sparse Probability Measures via Convex Programming P. Olsen, F. Oztoprak, J. Nocedal, S. Rennie Th34 Newton-Like Methods for Sparse Inverse Covariance Estimation S. Becker, J. Fadili Th35 A quasi-Newton proximal splitting method K. Jamieson, R. Nowak, B. Recht Th36 Query Complexity of Derivative-Free Optimization H. Ohlsson, A. Yang, R. Dong, S. Sastry Th37 CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem X. Zhang, L. Carin Th38 Joint Modeling of a Matrix with Associated Text via Latent Binary Features S. Babacan, S. Nakajima, M. Do Th39 Probabilistic Low-Rank Subspace Clustering K. Swersky, D. Tarlow, R. Zemel, R. Adams, B. Frey Th40 Bayesian n-Choose-k Models for Classification and Ranking F. Ruiz, I. Valera, C. Blanco, F. Perez-Cruz Th41 Bayesian Nonparametric Modeling of Suicide Attempts F. Caron Th42 Bayesian nonparametric models for bipartite graphs D. Lin, J. Fisher Th43 Coupling Nonparametric Mixtures via Latent Dirichlet Processes E. Fox, D. Dunson Th44 Multiresolution Gaussian Processes M. Lázaro-Gredilla Th45 Bayesian Warped Gaussian Processes N. Houlsby, J. Hernández-Lobato, F. Huszar, Z. Ghahramani Th46 Collaborative Gaussian Processes for Preference Learning G. Elidan, C. Cario Th47 Nonparanormal Belief Propagation (NPBP) M. Der, L. Saul Th48 Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning A. Guzmán-Rivera, D. Batra, P. Kohli Th49 Multiple Choice Learning: Learning to Produce Multiple Structured Outputs S. Gopal, Y. Yang, B. Bai, A. Niculescu-Mizil Th51 Bayesian models for Large-scale Hierarchical Classification H. Kadri, A. Rakotomamonjy, F. Bach, p. preux Th52 Multiple Operator-valued Kernel Learning K. Fukumizu, C. Leng Th53 Gradient-based kernel method for feature extraction and variable selection K. Muandet, K. Fukumizu, F. Dinuzzo, B. Schölkopf Th54 Learning from Distributions via Support Measure Machines R. Foygel, M. Horrell, M. Drton, J. Lafferty Th55 Nonparametric Reduced Rank Regression Y. Wiener, R. El-Yaniv Th56 Pointwise Tracking the Optimal Regression Function E. Richard, S. Gaiffas, N. Vayatis Th57 Link Prediction in Graphs with Autoregressive Features S. Kpotufe, A. Boularias Th58 Gradient Weights help Nonparametric Regressors A. Das, A. Dasgupta, R. Kumar Th59 Selecting Diverse Features via Spectral Regularization A. Argyriou, R. Foygel, N. Srebro Th60 Sparse Prediction with the $k$-Support Norm A. Dalalyan, Y. Chen Th61 Fused sparsity and robust estimation for linear models with unknown variance D. Wipf Th62 Dual-Space Analysis of the Sparse Linear Model P. Li, C. Zhang Th63 Entropy Estimations Using Correlated Symmetric Stable Random Projections D. Ryabko, J. Mary Th64 Reducing statistical time-series problems to binary classification C. Gentile, F. Orabona Th65 On Multilabel Classification and Ranking with Partial Feedback T. Yang, Y. Li, M. Mahdavi, R. Jin, Z. Zhou Th66 Nystr{ö}m Method vs Random Fourier Features: A Theoretical and Empirical Comparison G. Canas, T. Poggio, L. Rosasco Th67 Learning Manifolds with K-Means and K-Flats Q. Gu, T. Zhang, C. Ding, J. Han Th68 Selective Labeling via Error Bound Minimization J. Yi, R. Jin, A. Jain, S. Jain Th69 Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning D. Luo, C. Ding, H. Huang Th70 Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach M. Norouzi, R. Salakhutdinov, D. Fleet Th71 Hamming Distance Metric Learning J. Wang, A. Kalousis, A. Woznica Th72 Parametric Local Metric Learning for Nearest Neighbor Classification D. Kedem, S. Tyree, K. Weinberger, F. Sha, G. Lanckriet Th73 Non-linear Metric Learning Q. Jiang, J. Zhu, M. Sun, E. Xing Th74 Monte Carlo Methods for Maximum Margin Supervised Topic Models P. Krafft, J. Moore, H. Wallach, B. Desmarais Th75 Topic-Partitioned Multinetwork Embeddings O. Vinyals, Y. Jia, L. Deng, T. Darrell Th76 Learning with Recursive Perceptual Representations D. Zoran, Y. Weiss Th77 Natural Images, Gaussian Mixtures and Dead Leaves W. Zou, A. Ng, S. Zhu, K. Yu Th78 Deep Learning of invariant features via tracked video sequences x. wang, L. Lin Th79 Dynamical And-Or Graph Learning for Object Shape Modeling and Detection B. Alexe, N. Heess, Y. Teh, V. Ferrari Th80 Searching for objects driven by context T. Trzcinski, M. Christoudias, V. Lepetit, P. Fua Th81 Learning Image Descriptors with the Boosting-Trick M. Lopes, T. Lang, M. Toussaint, P. Oudeyer Th82 Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress R. Bourdoukan, D. Barrett, C. Machens, S. Deneve Th83 Learning optimal spike-based representations f. janoos, W. Li, N. Subrahmanya, I. Morocz, W. Wells Th84 Identification of Recurrent Patterns in the Activation of Brain Networks B. Vintch, A. Zaharia, J. Movshon, E. Simoncelli Th85 Efficient and direct estimation of a neural subunit model for sensory coding Y. Huang, A. Friesen, T. Hanks, M. Shadlen, R. Rao Th86 How Prior Probability Influences Decision Making: A Unifying Probabilistic Model S. Bohte Th87 Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model H. Terashima, M. Okada Th88 The topographic unsupervised learning of natural sounds in the auditory cortex P. Shenoy, A. Yu Th89 Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making C. Fung, K. Wong, S. Wu Th90 Delay Compensation with Dynamical Synapses D. Chklovskii Th91 Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter J. Beck, K. Heller, A. Pouget Th92 Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models A. Krizhevsky, I. Sutskever, G. Hinton Th25 ImageNet Classification with Deep Convolutional Neural Networks D. Zhou, J. Platt, S. Basu, Y. Mao Th50 Learning from the Wisdom of Crowds by Minimax Entropy

Workshops (December 7-8, 2012)

Friday, December 7
6:30 - 8:00am	Breakfast
7:00 - 11:00am	Registration Desk
7:30am - 6:30pm	Michael Goodrich, Pavel N Krivitsky, David M Mount, Christopher DuBois, Padhraic Smyth Algorithmic and Statistical Approaches for Large Social Network Data Sets
7:30am - 6:30pm	Martin Kleinsteuber, Francis Bach, Remi Gribonval, John Wright, Simon Hawe Analysis Operator Learning vs. Dictionary Learning: Fraternal Twins in Sparse Modeling
7:30am - 6:30pm	Javad Azimi, Roman Garnett, Frank R Hutter, Shakir Mohamed Bayesian Optimization and Decision Making
7:30am - 6:30pm	Jia Deng, Samy Bengio, Yuanqing Lin, Fei Fei F Li Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval
7:30am - 6:30pm	Viren Jain, Moritz Helmstaedter Connectomics: Opportunities and Challenges for Machine Learning
7:30am - 6:30pm	Stefanie Jegelka, Andreas Krause, Jeff A Bilmes, Pradeep K Ravikumar Discrete Optimization in Machine Learning (DISCML): Structure and Scalability
7:30am - 6:30pm	Theodoros Damoulas, Thomas Dietterich, Edith Law, Serge Belongie Human Computation for Science and Computational Sustainability
7:30am - 6:30pm	Naftali Tishby, Daniel Polani, Tobias Jung Information in Perception and Action
7:30am - 6:30pm	Jean-Philippe Vert, Anna Goldenberg, Christina S Leslie Machine Learning in Computational Biology
7:30am - 6:30pm	Georg Langs, Irina Rish, Guillermo Cecchi, Brian Murphy, Bjoern Menze, Kai-min K Chang, Moritz Grosse-Wentrup MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day)
7:30am - 6:30pm	Sivaraman Balakrishnan, Arthur Gretton, Mladen Kolar, John Lafferty, Han Liu, Tong Zhang Modern Nonparametric Methods in Machine Learning
7:30am - 6:30pm	Yevgeny Seldin, Guy Lever, John S Shawe-Taylor, Nicolò Cesa-Bianchi, Koby Crammer, Francois Laviolette, Gabor Lugosi, Peter L Bartlett Multi-Trade-offs in Machine Learning
7:30am - 6:30pm	Vikash K Mansinghka, Daniel M Roy, Noah Goodman Probabilistic Programming: Foundations and Applications (2 day)
7:30am - 6:30pm	Edoardo M Airoldi, David S Choi, Khalid El-Arini, Jure Leskovec Social network and social media analysis: Methods, models and applications
7:30am - 6:30pm	Ankur P Parikh, Le Song, Eric P Xing Spectral Algorithms for Latent Variable Models
7:30am - 6:30pm	Achim Rettinger, Marko Grobelnik, Blaz Fortuna, Xavier Carreras, Juanzi Li xLiTe: Cross-Lingual Technologies
3:30 - 6:30pm	Registration Desk
Saturday, December 8
6:30 - 8:00am	Breakfast
7:00 - 11:00am	Registration Desk
7:30am - 6:30pm	Sivaraman Balakrishnan, Alessandro Rinaldo, Donald Sheehy, Aarti Singh, Larry Wasserman Algebraic Topology and Machine Learning
7:30am - 6:30pm	Jonathan How, Lawrence Carin, John W Fisher, Michael I Jordan, Alborz Geramifard Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty
7:30am - 6:30pm	Sameer Singh, John Duchi, Yucheng Low, Joseph E Gonzalez Big Learning : Algorithms, Systems, and Tools
7:30am - 6:30pm	Le Song, Arthur Gretton, Alexander J Smola Confluence between Kernel Methods and Graphical Models
7:30am - 6:30pm	Yoshua Bengio, James S Bergstra, Quoc V. Le Deep Learning and Unsupervised Feature Learning
7:30am - 6:30pm	Dimitri Kanevsky, Tony Jebara, Li Deng, Stephen Wright, Georg Heigold, Avishy Carmi Log-Linear Models
7:30am - 6:30pm	Katherine M Ellis, Gert Lanckriet, Tommi Jaakkola, Lenny Grokop Machine Learning Approaches to Mobile Context Awareness
7:30am - 6:30pm	Georg Langs, Irina Rish, Guillermo Cecchi, Brian Murphy, Bjoern Menze, Kai-min K Chang, Moritz Grosse-Wentrup MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day)
7:30am - 6:30pm	Suvrit Sra, Alekh Agarwal Optimization for Machine Learning
7:30am - 6:30pm	Michael C Mozer, javier r movellan, Robert V Lindsey, Jacob Whitehill Personalizing education with machine learning
7:30am - 6:30pm	Tamir Hazan, George Papandreou, Danny Tarlow Perturbations, Optimization, and Statistics
7:30am - 6:30pm	Philipp Hennig, John P Cunningham, Michael A Osborne Probabilistic Numerics
7:30am - 6:30pm	Vikash K Mansinghka, Daniel M Roy, Noah Goodman Probabilistic Programming: Foundations and Applications (2 day)
7:30am - 6:30pm	Behrouz Touri, Olgica Milenkovic, Faramarz Fekri Social Choice: Theory and Practice
7:00 - 10:00pm	Reception

The schedule was last modified on 2013-1-22 11:31 PST