Monday, June 17
8:30-10:00	Keynote Speaker: Carlos Guestrin
10:30-12:10	Deep Learning 1 International 7, 8, 9, 10	Compressed Sensing 1 International 4, 5	Reinforcement Learning 1 International 1, 2, 3	Social Networks International 6
	On autoencoder scoring Kamyshanska, Memisevic	Feature Selection in High-Dimensional Classification Kolar, Liu	Learning Policies for Contextual Submodular Prediction Ross, Zhou, Yue, Dey, Bagnell	Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events Friedland, Jensen, Lavine
	On the difficulty of training Recurrent Neural Networks Pascanu, Mikolov, Bengio	Markov Network Estimation From Multi-attribute Data Kolar, Liu, Xing	Learning an Internal Dynamics Model from Control Demonstration Golub, Chase, Yu	Mixture of Mutually Exciting Processes for Viral Diffusion Yang, Zha
	Maxout Networks Goodfellow, Warde-Farley, Mirza, Courville, Bengio	Exact Rule Learning via Boolean Compressed Sensing Malioutov, Varshney	Safe Policy Iteration Pirotta, Restelli, Pecorino, Calandriello	Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks Heaukulani, Zoubin
	Collaborative hyperparameter tuning Bardenet, Brendel, Kegl, Sebag	Sparse Recovery under Linear Transformation Liu, Yuan, Ye	Temporal Difference Methods for the Variance of the Reward To Go Tamar, Castro, Mannor	Modeling Information Propagation with Survival Theory Gomez-Rodriguez, Leskovec, Schölkopf
	SL: Learning mid-level representations of objects by harnessing the aperture problem Memisevic, Exarchakis	Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery Chen, Caramanis	SL: Value Iteration with incremental representation learning for continuous POMDPs Brechtel, Gindele, Dillmann	SL: Learning Triggering Kernels for Multi-dimensional Hawkes Processes Zhou, Song, Zha
	SL: Approximation properties of DBNs with binary hidden units and real-valued visible units Krause, Fischer, Glasmachers, Igel		SL: The Sample-Complexity of General Reinforcement Learning Lattimore, Hutter, Sunehag	SL: Causal Estimation of Peer Influence Effects
	SL: Better Mixing via Deep Representations Bengio, Mesnil, Dauphin, Rifai		SL: Online Feature Selection for Model-based Reinforcement Learning Nguyen, Li, Silander, Leong	SL: Modeling Temporal Evolution and Multiscale Structure in Networks Herlau, Mørup, Schmidt
	SL: Fast dropout training Wang, Manning		SL: Bayesian Learning of Recursively Factored Environments Bellemare, Veness, Bowling	SL: Scalable Optimization of Neighbor Embedding for Visualization Yang, Peltonen, Kaski
2:00-3:40	Deep Learning 2 International 7, 8, 9, 10	Compressed Sensing 2 International 4, 5	Reinforcement learning 2 International 1, 2, 3	Topic Modeling 1 Topic Modeling 1
	Learning the Structure of Sum-Product Networks Gens, Pedro	Learning Heteroscedastic Models by Convex Programming under Group Sparsity Dalalyan, Hebiri, Meziani, Salmon	Concurrent Reinforcement Learning from Customer Interaction Sequences Silver, Newnham, Barker, McFall	A Practical Algorithm for Topic Modeling with Provable Guarantees Arora, Ge, Halpern, Mimno, Moitra, Sontag, Wu, Zhu
	Deep learning with COTS HPC systems Coates, Huval, Wang, Wu, Catanzaro, Andrew	Noisy Sparse Subspace Clustering Wang, Xu	Modelling Sparse Dynamical Systems with Compressed Predictive State Representations Hamilton, Fard,, Pineau,	Online Latent Dirichlet Allocation with Infinite Vocabulary ZHAI, Boyd-Graber
	Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines Sohn, Zhou, Lee, Lee	One-Bit Compressed Sensing: Provable Support and Vector Recovery Gopi, Netrapalli, Jain, Nori	Coco-Q: Learning in Stochastic Games with Side Payments Hilliard, Sodomka, Littman, Greenwald	Gibbs Max-Margin Topic Models with Fast Sampling Algorithms Zhu, Chen, Perkins, Zhang
	Regularization of Neural Networks using DropConnect Wan, Zeiler, Zhang, Cun, Fergus	Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations Balasubramanian, Yu, Lebanon	Guided Policy Search Levine, Koltun	SL: Modeling Musical Influence with Topic Models Shalit, Weinshall, Chechik
	SL: Thurstonian Boltzmann Machines: Learning from Multiple Inequalities Tran, Phung, Venkatesh	SL: Sparse projections onto the simplex Kyrillidis, Becker, Cevher, Koch	The Cross-Entropy Method Optimizes for Quantiles Goschin, Weinstein, Littman	SL: Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling Ahmed, Hong, Smola
	SL: Iterative Learning and Denoising in Convolutional Neural Associative Memories Karbasi, Salavati, Shokrollahi,	SL: Intersecting singularities for multi-structured estimation Richard, BACH, Vert		SL: Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models Williamson, Dubey, Xing
	SL: No more pesky learning rates Schaul, Zhang, LeCun	SL: Sparse Uncorrelated Linear Discriminant Analysis Zhang, Chu		SL: MAD-Bayes: MAP-based Asymptotic Derivations from Bayes Broderick, Kulis, Jordan
	SL: Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures Bergstra, Yamins, Cox	SL: Estimating Unknown Sparsity in Compressed Sensing Lopes		SL: Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment Chuang, Gupta, Manning, Heer
4:00-5:40	Deep Learning and Neuroscience International 7, 8, 9, 10	Compressed Sensing 3 International 4, 5	Reinforcement Learning and Time Series International 1, 2, 3	Topic Modeling 2 International 6
	On the importance of initialization and momentum in deep learning Sutskever, Martens, Dahl, Hinton	Spectral Compressed Sensing via Structured Matrix Completion Chen, Chi	ABC Reinforcement Learning Dimitrakakis, Tziortziotis	Dependent Normalized Random Measures Chen, Rao, Teh, Buntine
	A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Georgiev, Nakov	Sparse PCA through Low-rank Approximations Papailiopoulos, Dimakis, Korokythakis	Mean Reversion with a Variance Threshold Cuturi, d’Aspremont	Topic Discovery through Data Dependent and Random Projections Ding, Rohban, Ishwar, Saligrama
	Parsing epileptic events using a Markov switching process model for correlated time series Wulsin, Fox, Litt	Efficient Sparse Group Feature Selection via Nonconvex Optimization Xiang, Shen, Ye	Gaussian Process Kernels for Pattern Discovery and Extrapolation Wilson, Adams	Factorial Multi-Task Learning : A Bayesian Nonparametric Approach Gupta, Phung, Venkatesh
	Exploring the Mind: Integrating Questionnaires and fMRI Salazar, Bogdan, Gorka, Hariri, Carin	A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems Gong, Zhang, Lu, Huang, Ye	SL: Average Reward Optimization Objective In Partially Observable Domains Grinberg, Precup	SL: Scaling the Indian Buffet Process via Submodular Maximization Reed, Zoubin
	SL: Gated Autoencoders with Tied Input Weights Droniou, Sigaud	Robust Sparse Regression under Adversarial Corruption Chen, Caramanis, Mannor	SL: Planning by Prioritized Sweeping with Small Backups Seijen, Sutton	SL: A Variational Approximation for Topic Modeling of Hierarchical Corpora Kim, Voelker, Saul
	SL: Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images Cho		SL: Dynamic Covariance Models for Multivariate Financial Time Series Wu, Hernandez-Lobato, Zoubin	SL: Manifold Preserving Hierarchical Topic Models for Quantization and Approximation Kim, Smaragdis
	SL: Natural Image Bases to Represent Neuroimaging Data Gupta, Ayhan, Maida		SL: Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression Hocking, Rigaill, VERT, BACH	SL: Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically Das, Bhattacharya, Bhattacharyya, Kanchi
	SL: Direct Modeling of Complex Invariances for Visual Object Features Hui		SL: Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data Meent, Bronson, Wood, Jr., Wiggins	SL: Latent Dirichlet Allocation Topic Model with Soft Assignment of Descriptors to Words Weinshall, Levi, Hanukaev
			SL: Learning Connections in Financial Time Series Ganeshapillai, Guttag, Lo	SL: Efficient Multi-label Classification with Many Labels Bi, Kwok
			SL: The Extended Parameter Filter, Yusuf Bugra Erol; Li, Ramsundar, Stuart	SL: A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning Afkanpour, Gyorgy, Szepesvari, Bowling
			SL: Transition Matrix Estimation in High Dimensional Time Series Han, Liu	SL: MILEAGE: Multiple Instance LEArning with Global Embedding Zhang, He, Si, Lawrence
Tuesday, June 18
8:30-10:00	Keynote Speaker: Santosh Vempala
10:30-12:10	Online Learning 1 International 7, 8, 9, 10	Feature Learning International 4, 5	General SVM and Decision Tree Methods International 1, 2, 3	Spectral Learning and Tensors International 6
	Online Kernel Learning with a Near Optimal Sparsity Bound Zhang, Jin, He	Forecastable Component Analysis Goerg	Multi-Class Classification with Maximum Margin Multiple Kernel Cortes, Mohri, Rostamizadeh	Spectral Learning of Hidden Markov Models from Dynamic and Static Data Huang, Schneider
	On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions Jain, Sriperumbudur, Kar, Karnick	Discriminatively Activated Sparselets Girshick, Song, Darrell	Top-down particle filtering for Bayesian decision trees Lakshminarayanan, Roy, Teh	Learning Linear Bayesian Networks with Latent Variables, Animashree Anandkumar; Daniel Hsu; Javanmard, Kakade
	Thompson Sampling for Contextual Bandits with Linear Payoffs Agrawal, Goyal	Multi-View Clustering and Feature Learning via Structured Sparsity Wang, Nie, Huang	Cost-Sensitive Tree of Classifiers Xu, Kusner, Weinberger, Chen	Spectral Experts for Estimating Mixtures of Linear Regressions Chaganty, Liang
	Online Learning under Delayed Feedback Joulani, Gyorgy, Szepesvari	Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation Gong, Grauman, Sha	SL: On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance Menon, Narasimhan, Agarwal, Chawla	On learning parametric-output HMMs, Aryeh Kontorovich; Nadler, Weiss
	Almost Optimal Exploration in Multi-Armed Bandits Karnin, Koren, Somekh	SL: On Nonlinear Generalization of Sparse Coding and Dictionary Learning Ho, Xie, Vemuri	SL: Quickly Boosting Decision Trees — Pruning Underachieving Features Early Appel, Fuchs, Dollar, Perona	SL: Tensor Analyzers, Yichuan Tang; Ruslan Salakhutdinov ; Hinton
		SL: Feature Multi-Selection among Subjective Features Sabato, Kalai	SL: Tree-Independent Dual-Tree Algorithms Curtin, March, Ram, Anderson, Gray, Isbell	SL: Unfolding Latent Tree Structures using 4th Order Tensors Ishteva, Park, Song
		SL: Sparsity-Based Generalization Bounds for Predictive Sparse Coding Mehta, Gray	SL: Loss-Proportional Subsampling for Subsequent ERM Mineiro, Karampatziakis	SL: Hierarchical Tensor Decomposition of Latent Tree Graphical Models, Le Song; Ishteva, Parikh, Xing, Park
		SL: A Unified Robust Regression Model for Lasso-like Algorithms Yang, Xu	SL: Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner Sun, Zhou	SL: Infinite Positive Semidefinite Tensor Factorization with Application to Music Signal Analysis Yoshii, Tomioka, Mochihashi, Goto
			SL: Safe Screening of Non-Support Vectors in Pathwise SVM Computation Ogawa, Suzuki, Takeuchi
			SL: Convex formulations of radius-margin based Support Vector Machines Do, Kalousis
			SL: The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification Pele, Taskar, Globerson, Werman
2:00-3:40	Online Learning 2 International 7, 8, 9, 10	Structured Labeling International 4, 5	Dimensionality Reduction International 1, 2, 3	Statistical Methods International 6
	Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning Maillard, Nguyen, Ortner, Ryabko	Learning from Human-Generated Lists Jun, Zhu, Settles, Rogers	Principal Component Analysis on non-Gaussian Dependent Data Han, Liu	Computation-Risk Tradeoffs for Covariance-Thresholded Regression Shender, Lafferty
	Combinatorial Multi-Armed Bandit: General Framework, Results and Applications Chen, Wang, Yuan	A Structural SVM Based Approach for Optimizing Partial AUC Narasimhan, Agarwal	Deep Canonical Correlation Analysis Andrew, Bilmes, Arora, Livescu	Scalable Simple Random Sampling and Stratified Sampling Meng
	Dynamical Models and tracking regret in online convex programming Hall, Willett	A Machine Learning Framework for Programming by Example Menon, Tamuz, Gulwani, Lampson, Kalai	Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment Chang, Kruger, Kustra, Zhang	The lasso, persistence, and cross-validation Homrighausen, McDonald
	Better Rates for Any Adversarial Deterministic MDPs Dekel, Hazan	Convex Adversarial Collective Classification Torkamani, Lowd	Vanishing Component Analysis Livni, Lehavi, Schein, Nachliely, Shalev-Shwartz, Globerson	Consistency versus Realizable H-Consistency for Multiclass Classification Long, Servedio
	SL: Multiple Identifications in Multi-Armed Bandits Bubeck, Wang, Viswanathan	SL: Learning Convex QP Relaxations for Structured Prediction Jancsary, Nowozin, Rother	SL: Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration Kuleshov	SL: Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy Honorio, Tommi
	SL: Gossip-based distributed stochastic bandit algorithms Szorenyi, Busa-Fekete, Hegedus, Ormandi, Jelasity, Kegl	SL: Fixed-Point Model For Structured Labeling Li, Wang, Wipf, Tu	SL: Efficient Dimensionality Reduction for Canonical Correlation Analysis Avron, , , Zouzias	SL: Scale Invariant Conditional Dependence Measures Reddi, Poczos
	SL: Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method Suzuki	SL: A Generalized Kernel Approach to Structured Output Learning Kadri, Ghavamzadeh, Preux	SL: Adaptive Sparsity in Gaussian Graphical Models Wong, Awate, Fletcher	SL: Infinite Markov-Switching Maximum Entropy Discrimination Machines Chatzis
		SL: Optimizing the F-measure in Multi-label Classification: Plug-in Rule Approach versus Structured Loss Minimization Dembczynski, Kotlowski, Jachnik, Waegeman, Huellermeier	SL: The Most Generative Maximum Margin Bayesian Networks Peharz, Tschiatschek, Pernkopf	SL: Distribution to Distribution Regression Oliva, Poczos, Schneider
4:00-5:40	Nearest Neighbor and Metric Learning International 7, 8, 9, 10	General Methods General Methods	Transfer Learning International 1, 2, 3	Statistical Learning and Inference International 6
	Entropic Affinities: Properties and Efficient Numerical Computation Vladymyrov, Carreira-Perpinan	Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model Xiao, Guo	Domain Generalization via Invariant Feature Representation Muandet, , Schoelkopf	Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs S., Bach
	Learning Hash Functions Using Column Generation Li, Lin, Shen, Hengel, Dick	Maximum Variance Correction with Application to A* Search Chen, Weinberger, Chen	A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers Germain, Habrard, Laviolette, Morvant	∝  SVM for Learning with Label Proportions Yu, Liu, Kumar, Tony, Chang
	Robust Structural Metric Learning Lim, Lanckriet, McFee	Learning with Marginalized Corrupted Features Maaten, Chen, Tyree, Weinberger	Sparse coding for multitask and transfer learning Maurer, Pontil, Romera-Paredes	Consistency of Online Random Forests Denil, Matheson, Nando
	Revisiting the Nystrom method for improved large-scale machine learning Gittens, Mahoney	SL: Scaling Multidimensional Gaussian Processes using Projected Additive Approximations Gilboa, Saatci, Cunningham	Bayesian Games for Adversarial Regression Problems Groáhans, Sawade, Brückner, Scheffer	SL: Inference algorithms for pattern-based CRFs on sequence data Takhanov, Kolmogorov
	SL: That was fast! Speeding up NN search of high dimensional distributions Coviello, Mumtaz, Chan, Lanckriet	SL: Nonparametric Mixture of Gaussian Processes with Constraints Ross, Dy	SL: Joint Transfer and Batch-mode Active Learning Chattopadhyay, Fan, Davidson, Panchanathan, Ye	SL: Relaxed expectation propagation based on l1-penalized KL minimization Qi, Guo
	SL: Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning Tarlow, Swersky, Sutskever, Charlin, Zemel	SL: Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models Khan, Aravkin, Friedlander, Seeger	SL: Multilinear Multitask Learning Romera-Paredes, Aung, Bianchi-Berthouze, Pontil	SL: A Fast and Exact Energy Minimization Algorithm for Cycle MRFs Wang, Daphne
	SL: Predictable Dual-View Hashing Rastegari, Choi, Fakhraei, Hal, Davis	SL: Gaussian Process Vine Copulas for Multivariate Dependence Lopez-Paz, Hernandez-Lobato, Zoubin	SL: Stability and Hypothesis Transfer Learning Kuzborskij, Orabona	SL: Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing, Huayan Wang; Koller Daphne Wang, Daphne
	SL: A unifying framework for vector-valued manifold regularization and multi-view learning Quang, Bazzani, Murino	SL: Structure Discovery in Nonparametric Regression through Compositional Kernel Search Duvenaud, Lloyd, Grosse, Tenenbaum, Zoubin	SL: Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model Yang, Yingming, (Mark)	SL: Approximate Inference in Collective Graphical Models Sheldon, Sun, Kumar, Dietterich
		SL: Sequential Bayesian Search Wen, Kveton, Eriksson, Bhamidipati		SL: An Adaptive Learning Rate for Stochastic Variational Inference Ranganath, Wang, David, Xing
		SL: Kernelized Bayesian Matrix Factorization Gönen, Khan, Kaski		SL: The Bigraphical Lasso Kalaitzis, Lafferty, Lawrence
		SL: SADA: A General Framework to Support Robust Causation Discovery Cai, Zhang, Hao		SL: Anytime Representation Learning Xu, Kusner, Huang, Weinberger
Wednesday, June 19
8:30-10:00	Keynote Speaker: Vincent Vanhoucke
10:30-12:10	Invited Orals International 7, 8, 9, 10	Optimization International 4, 5	Clustering International 1, 2, 3	Learning Theory 1 International 6
	Classic Paper Prize Talk: Semi-supervised learning using gaussian fields and harmonic functions (ICML 2003)	Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Shamir, Zhang	A Local Algorithm for Finding Well-Connected Clusters Lattanzi, Mirrokni, Zhu	Margins, Shrinkage and Boosting Telgarsky
	Classic Paper Prize Talk: Online convex programming and generalized infinitesimal gradient ascent (ICML 2003)	Optimal rates for stochastic convex optimization under Tsybakov noise condition Ramdas, Singh	Monochromatic Bi-Clustering Wulff, Urner, Ben-David	Sharp Generalization Error Bounds for Randomly-projected Classifiers Durrant, Kaban
	What do we learn from Kaggle competitions? Hamner	Fast Semidifferential-based Submodular Function Optimization Iyer, Jegelka, Bilmes	Constrained fractional set programs and their application in local clustering and community detection Bühler, Rangapuram, Setzer, Hein	Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction Giguère, Laviolette, Marchand, Sylla
	Machine Learning and Natural Langauge Processing Liang	A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions Dinh, Kyrillidis, Cevher	Breaking the Small Cluster Barrier of Graph Clustering Ailon, Chen, Xu	Collective Stability and Structured Prediction: Generalization from One Example London, Huang, Taskar, Getoor
	Large-Scale Bandit Problems and KWIK Learning Abernethy, Amin, Kearns, Draief	SL: Mini-Batch Primal and Dual Methods for SVMs Takac, Bijral, Richtarik, Srebro	SL: Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings Rebagliati	SL: Hierarchical Regularization Cascade for Joint Learning Zweig, Weinshall
		SL: Stochastic Alternating Direction Method of Multipliers Ouyang, He, Tran, Gray	SL: Clustering and Learning Behaviors using a Sparse Latent Space Montesano, Lopes, Almingol	SL: Learning Fair Representations Zemel, Wu, Swersky, Pitassi, Dwork
		SL: Optimization with First-Order Surrogate Functions Mairal	SL: Precision-recall space to correct external indices for biclustering Hanczar, Nadif	SL: Differentially Private Learning with Kernels Jain, Thakurta
		SL: Fast Probabilistic Optimization from Noisy Gradients Hennig	SL: Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion Yi, Jin, Qian, Jain	SL: Rounding Methods for Discrete Linear Classification Chevaleyre, Koriche, Zucker
2:00-3:40	Dimensionality Reduction and Semi-Supervised Learning International 7, 8, 9, 10	Optimization and Integration International 4, 5	Vision International 1, 2, 3	Learning Theory II International 1, 2, 3
	Squared-loss Mutual Information Regularization Niu, Jitkrittum, , Hachiya, Sugiyama	Stochastic Simultaneous Optimistic Optimization Valko, Carpentier, Munos	An Optimal Policy for Target Localization with Application to Electron Microscopy Sznitman, Lucchi, Frazier, Jedynak, Fua	Exploiting Ontology Structures and Unlabeled Data for Learning Balcan, Blum, Mansour
	Ellipsoidal Multiple Instance Learning Krummenacher, Ong, Buhmann	Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Lacoste-Julien, Jaggi, Schmidt, Pletscher	Fast Image Tagging Chen, Zheng, Weinberger	One-Pass AUC Optimization, Wei Gao; Rong Jin; Shenghuo Zhu; Zhi-Hua Zhou Gao, Jin, Zhu, Zhou
	Infinitesimal Annealing for Training Semi-Supervised Support Vector Machine Ogawa, Imamura, Takeuchi, Sugiyama	Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization Ermon, Gomes, Sabharwal, Selman	An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation Bryan, Mysore	Near-Optimal Bounds for Cross-Validation via Loss Stability Kumar, Lokshtanov, Vassilvitskii, Vattani,
	Sparse Gaussian Conditional Random Fields: Algorithms, and Application to Energy Forecasting Wytock, Kolter	SL: Expensive Function Optimization with Stochastic Binary Outcomes Tesch, Schneider, Choset	SL: Max-Margin Multiple-Instance Dictionary Learning Wang, Tu	Algebraic classifiers: a generic approach to fast cross-validation,parallel training Izbicki
	Adaptive Hamiltonian and Riemann Manifold Monte Carlo Wang, Mohamed, Nando	SL: O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions Zhang, Yang, Jin, He	SL: Parameter Learning and Convergent Inference for Dense Random Fields Kraehenbuehl, Koltun	SL: Top-k Selection based on Adaptive Sampling of Noisy Preferences Busa-Fekete, Cheng, Weng, Huellermeier, Szorenyi
		SL: Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization Jaggi	SL: Can We Recognize Tiger by Bus Images? _ Robust and Discriminative Self-Taught Image Categorization Wang, Nie, Huang	SL: Enhanced statistical rankings via targeted data collection Osting, Brune, Osher
		SL: Algorithms for Direct 0–1 Loss Optimization in Binary Classification Nguyen, Sanner	SL: Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Forecasting Koppula, Saxena	SL: Efficient Ranking from Pairwise Comparisons Wauthier, Jordan, Jojic
		SL: Toward Optimal Stratification for Stratified Monte-Carlo Integration Carpentier, Munos	SL: A Spectral Learning Approach to Range-Only SLAM, Byron Boots; Gordon	SL: Stable Coactive Learning via Perturbation Raman, Joachims,, Shivaswamy, Schnabel
			SL: Non-Linear Stationary Subspace Analysis with Application to Video Classification Baktashmotlagh, Harandi, Bigdeli, Lovell, Salzmann
			SL: On Compact Codes for Spatially Pooled Features Jia, Vinyals, Darrell
			SL: Analogy-preserving Semantic Embedding for Visual Object Categorization Hwang, Grauman, Sha
4:00-5:40	Crowd Sourcing and Large Scale Learning International 7, 8, 9, 10	Kernel Methods International 4, 5	Matrix Factorization Matrix Factorization	Learning Theory 3 International 6
	Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing Chen, Lin, Zhou	Local Deep Kernel Learning for Efficient Non-linear SVM Prediction Jose, Goyal, Aggrwal, Varma	Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization Kumar, Sindhwani, Kambadur	Activized Learning with Uniform Classification Noise Yang, Hanneke
	Quantile Regression for Large-scale Applications Mahoney, Yang, Meng	Fastfood – Computing Hilbert Space Expansions in loglinear time Le, Sarlos, Smola	General Functional Matrix Factorization Using Gradient Boosting Chen, Li, Yang, Yu	Efficient Active Learning of Halfspaces: an Aggressive Approach Gonen, Sabato, Shalev-Shwartz
	Distributed training of Large-scale Logistic models Gopal, Yang	Smooth Operators and an RKHS Integration Approach Grunewalder, Arthur, Shawe-Taylor	Fast Max-Margin Matrix Factorization with Data Augmentation Xu, Zhu, Zhang	Selective sampling algorithms for cost-sensitive multiclass prediction Agarwa
	Label Partitioning For Sublinear Ranking Weston, Makadia, Yee	Domain Adaptation under Target and Conditional Shift Zhang, Schoelkopf, Muandet, Wang	Local Low-Rank Matrix Approximation Lee, Kim, Lebanon, Singer	Generic Exploration and K-armed Voting Bandits Urvoy, Clerot, Feraud, Naamane
	SL: Human Boosting Pareek, Ravikumar	SL: Learning Optimally Sparse Support Vector Machines Cotter, Shalev-Shwartz, Srebro	SL: Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models Simsekli, Yilmaz, Cemgil	SL: Efficient Semi-supervised and Active Learning of Disjunctions Balcan, Berlind, Ehrlich, Liang
	SL: Large-Scale Learning with Less RAM via Randomization Golovin, Sculley, McMahan, Young	SL: A New Frontier of Kernel Design for Structured Data Shin	SL: ELLA: An Efficient Lifelong Learning Algorithm Ruvolo, Eaton	SL: Cost-sensitive Multiclass Classification Risk Bounds Pires, Szepesvari, Ghavamzadeh
	SL: Robust Regression on MapReduce Mahoney, Meng	SL: Characterizing the Representer Theorem Yu, Cheng, Schuurmans, Szepesvari	SL: Riemannian Similarity Learning Cheng	SL: Active Learning for Multi-Objective Optimization Zuluaga, Sergent, Krause, Pueschel
	SL: Adaptive Task Assignment for Crowdsourced Classification Ho, Jabbari, Vaughan	SL: Covariate Shift in Hilber Space: A Solution Via Sorrogate Kernels Zhang, Zheng, Wang, Kwok, Yang	SL: Multiple-Source Cross Validation Geras, Sutton	SL: Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization Chen, Krause