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3D Object Proposals for Accurate Object Class Detection 210 C #12
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making 210 C #27
A class of network models recoverable by spectral clustering 210 C #76
A Nonconvex Optimization Framework for Low Rank Matrix Estimation 210 C #101
A Recurrent Latent Variable Model for Sequential Data 210 C #21
A Theory of Decision Making Under Dynamic Context 210 C #18
Accelerated Proximal Gradient Methods for Nonconvex Programming 210 C #96
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing 210 C #91
Adversarial Prediction Games for Multivariate Losses 210 C #54
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching 210 C #65
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks 210 C #69
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question 210 C #9
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions 210 C #50
Bandits with Unobserved Confounders: A Causal Approach 210 C #60
Basis refinement strategies for linear value function approximation in MDPs 210 C #62
Bayesian Optimization with Exponential Convergence 210 C #83
Bidirectional Recurrent Neural Networks as Generative Models 210 C #19
BinaryConnect: Training Deep Neural Networks with binary weights during propagations 210 C #15
Black-box optimization of noisy functions with unknown smoothness 210 C #89
Calibrated Structured Prediction 210 C #26
Combinatorial Cascading Bandits 210 C #90
Communication Complexity of Distributed Convex Learning and Optimization 210 C #99
Convergence Analysis of Prediction Markets via Randomized Subspace Descent 210 C #95
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling 210 C #2
Copula variational inference 210 C #37
Decomposition Bounds for Marginal MAP 210 C #68
Deep Knowledge Tracing 210 C #22
Deep Temporal Sigmoid Belief Networks for Sequence Modeling 210 C #23
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation 210 C #28
Differentially Private Learning of Structured Discrete Distributions 210 C #81
Discrete Rényi Classifiers 210 C #66
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing 210 C #45
Embedding Inference for Structured Multilabel Prediction 210 C #43
Estimating Mixture Models via Mixtures of Polynomials 210 C #70
Expectation Particle Belief Propagation 210 C #42
Explore no more: Improved high-probability regret bounds for non-stochastic bandits 210 C #100
Exploring Models and Data for Image Question Answering 210 C #8
Fast and Accurate Inference of Plackett–Luce Models 210 C #49
Fast Randomized Kernel Ridge Regression with Statistical Guarantees 210 C #84
Fast Second Order Stochastic Backpropagation for Variational Inference 210 C #38
Fast Two-Sample Testing with Analytic Representations of Probability Measures 210 C #53
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 210 C #6
GAP Safe screening rules for sparse multi-task and multi-class models 210 C #67
Generative Image Modeling Using Spatial LSTMs 210 C #5
Grammar as a Foreign Language 210 C #3
Halting in Random Walk Kernels 210 C #56
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks 210 C #32
Hidden Technical Debt in Machine Learning Systems 210 C #24
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability 210 C #102
Infinite Factorial Dynamical Model 210 C #35
Learning From Small Samples: An Analysis of Simple Decision Heuristics 210 C #11
Learning spatiotemporal trajectories from manifold-valued longitudinal data 210 C #31
Learning to Transduce with Unbounded Memory 210 C #16
Learning with a Wasserstein Loss 210 C #47
Learning with Relaxed Supervision 210 C #51
Lifted Symmetry Detection and Breaking for MAP Inference 210 C #59
Local Expectation Gradients for Black Box Variational Inference 210 C #46
M-Statistic for Kernel Change-Point Detection 210 C #52
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation 210 C #72
Minimax Time Series Prediction 210 C #98
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications 210 C #74
Model-Based Relative Entropy Stochastic Search 210 C #40
Monotone k-Submodular Function Maximization with Size Constraints 210 C #77
Nearly Optimal Private LASSO 210 C #97
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators 210 C #64
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors 210 C #87
Online Gradient Boosting 210 C #93
Online Prediction at the Limit of Zero Temperature 210 C #58
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation 210 C #10
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric 210 C #48
Probabilistic Variational Bounds for Graphical Models 210 C #63
Rate-Agnostic (Causal) Structure Learning 210 C #57
Recognizing retinal ganglion cells in the dark 210 C #20
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction 210 C #4
Regressive Virtual Metric Learning 210 C #55
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices 210 C #94
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees 210 C #88
Rethinking LDA: Moment Matching for Discrete ICA 210 C #39
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis 210 C #30
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso 210 C #71
Robust PCA with compressed data 210 C #73
Robust Portfolio Optimization 210 C #82
Sample Complexity Bounds for Iterative Stochastic Policy Optimization 210 C #61
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models 210 C #29
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods 210 C #33
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data 210 C #14
Smooth and Strong: MAP Inference with Linear Convergence 210 C #78
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction 210 C #80
Spectral Representations for Convolutional Neural Networks 210 C #17
Statistical Model Criticism using Kernel Two Sample Tests 210 C #25
StopWasting My Gradients: Practical SVRG 210 C #79
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector 210 C #75
Sum-of-Squares Lower Bounds for Sparse PCA 210 C #92
Supervised Learning for Dynamical System Learning 210 C #41
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms 210 C #85
Texture Synthesis Using Convolutional Neural Networks 210 C #1
The Poisson Gamma Belief Network 210 C #13
Tractable Learning for Complex Probability Queries 210 C #44
Variational Dropout and the Local Reparameterization Trick 210 C #34
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning 210 C #36
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis 210 C #7
Beyond Convexity: Stochastic Quasi-Convex Optimization 210 C #86

b-bit Marginal Regression Room 210 A
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements Room 210 A
Minimum Weight Perfect Matching via Blossom Belief Propagation Room 210 A
Optimal Rates for Random Fourier Features Room 210 A
Submodular Hamming Metrics Room 210 A
Super-Resolution Off the Grid Room 210 A
Top-k Multiclass SVM Room 210 A

Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Room 210 A
Distributed Submodular Cover: Succinctly Summarizing Massive Data Room 210 A
Distributionally Robust Logistic Regression Room 210 A
Extending Gossip Algorithms to Distributed Estimation of U-statistics Room 210 A
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees Room 210 A
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma Room 210 A
On some provably correct cases of variational inference for topic models Room 210 A
The Self-Normalized Estimator for Counterfactual Learning Room 210 A

Automatic Variational Inference in Stan Room 210 A
Data Generation as Sequential Decision Making Room 210 A
Deep learning with Elastic Averaging SGD Room 210 A
Latent Bayesian melding for integrating individual and population models Room 210 A
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Room 210 A
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width Room 210 A
Stochastic Expectation Propagation Room 210 A

Biologically Inspired Dynamic Textures for Probing Motion Perception Room 210 A
Closed-form Estimators for High-dimensional Generalized Linear Models Room 210 A
High-dimensional neural spike train analysis with generalized count linear dynamical systems Room 210 A
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Room 210 A
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Room 210 A
Measuring Sample Quality with Stein's Method Room 210 A
On the Pseudo-Dimension of Nearly Optimal Auctions Room 210 A
The Human Kernel Room 210 A

An interactive system for the extraction of meaningful visualizations from high-dimensional data 210D
Claudico: The World's Strongest No-Limit Texas Hold'em Poker AI 210D
Deep Learning using Approximate Hardware 210D
DIANNE - Distributed Artificial Neural Networks 210D
Fast sampling with neuromorphic hardware 210D
Vitruvian Science: a visual editor for quickly building neural networks in the cloud 210D
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements 210 C #93
A hybrid sampler for Poisson-Kingman mixture models 210 C #41
A Universal Primal-Dual Convex Optimization Framework 210 C #89
Alternating Minimization for Regression Problems with Vector-valued Outputs 210 C #76
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization 210 C #63
Automatic Variational Inference in Stan 210 C #34
b-bit Marginal Regression 210 C #81
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution 210 C #6
Biologically Inspired Dynamic Textures for Probing Motion Perception 210 C #14
Character-level Convolutional Networks for Text Classification 210 C #10
Closed-form Estimators for High-dimensional Generalized Linear Models 210 C #85
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution 210 C #23
Collaboratively Learning Preferences from Ordinal Data 210 C #74
Combinatorial Bandits Revisited 210 C #96
Community Detection via Measure Space Embedding 210 C #52
Competitive Distribution Estimation: Why is Good-Turing Good 210 C #88
Data Generation as Sequential Decision Making 210 C #35
Deep Generative Image Models using a ￼Laplacian Pyramid of Adversarial Networks 210 C #1
Deep learning with Elastic Averaging SGD 210 C #37
Deep Poisson Factor Modeling 210 C #16
Deeply Learning the Messages in Message Passing Inference 210 C #22
Distributed Submodular Cover: Succinctly Summarizing Massive Data 210 C #65
Distributionally Robust Logistic Regression 210 C #60
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression 210 C #27
Evaluating the statistical significance of biclusters 210 C #68
Expressing an Image Stream with a Sequence of Natural Sentences 210 C #4
Extending Gossip Algorithms to Distributed Estimation of U-statistics 210 C #72
Fast Bidirectional Probability Estimation in Markov Models 210 C #67
Fast Convergence of Regularized Learning in Games 210 C #97
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms 210 C #86
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees 210 C #57
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning 210 C #8
High-dimensional neural spike train analysis with generalized count linear dynamical systems 210 C #26
HONOR: Hybrid Optimization for NOn-convex Regularized problems 210 C #92
Inference for determinantal point processes without spectral knowledge 210 C #54
Interactive Control of Diverse Complex Characters with Neural Networks 210 C #13
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm 210 C #79
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings 210 C #17
Large-scale probabilistic predictors with and without guarantees of validity 210 C #49
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements 210 C #82
Latent Bayesian melding for integrating individual and population models 210 C #25
Learning Bayesian Networks with Thousands of Variables 210 C #45
Learning both Weights and Connections for Efficient Neural Network 210 C #12
Learning Continuous Control Policies by Stochastic Value Gradients 210 C #31
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels 210 C #32
Learning Structured Output Representation using Deep Conditional Generative Models 210 C #3
Learning visual biases from human imagination 210 C #9
Learning with Group Invariant Features: A Kernel Perspective. 210 C #38
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes 210 C #39
Matrix Manifold Optimization for Gaussian Mixtures 210 C #56
Measuring Sample Quality with Stein's Method 210 C #62
Minimum Weight Perfect Matching via Blossom Belief Propagation 210 C #80
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma 210 C #73
On Elicitation Complexity 210 C #98
On some provably correct cases of variational inference for topic models 210 C #48
On the Accuracy of Self-Normalized Log-Linear Models 210 C #50
On the Pseudo-Dimension of Nearly Optimal Auctions 210 C #84
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants 210 C #77
Online Learning with Adversarial Delays 210 C #99
Optimal Rates for Random Fourier Features 210 C #95
Parallel Correlation Clustering on Big Graphs 210 C #66
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions 210 C #46
Path-SGD: Path-Normalized Optimization in Deep Neural Networks 210 C #33
Planar Ultrametrics for Image Segmentation 210 C #44
Policy Evaluation Using the Ω-Return 210 C #51
Preconditioned Spectral Descent for Deep Learning 210 C #30
Private Graphon Estimation for Sparse Graphs 210 C #91
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process 210 C #29
Probabilistic Line Searches for Stochastic Optimization 210 C #40
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition 210 C #83
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width 210 C #47
Reflection, Refraction, and Hamiltonian Monte Carlo 210 C #43
Regularization Path of Cross-Validation Error Lower Bounds 210 C #69
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning 210 C #90
Sample Complexity of Learning Mahalanobis Distance Metrics 210 C #55
Sampling from Probabilistic Submodular Models 210 C #70
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients 210 C #58
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization 210 C #87
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk 210 C #75
Shepard Convolutional Neural Networks 210 C #2
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems 210 C #64
Stochastic Expectation Propagation 210 C #36
Structured Estimation with Atomic Norms: General Bounds and Applications 210 C #100
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals 210 C #7
Submodular Hamming Metrics 210 C #71
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's 210 C #101
Subset Selection by Pareto Optimization 210 C #78
Super-Resolution Off the Grid 210 C #94
Tensorizing Neural Networks 210 C #18
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions 210 C #20
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels 210 C #53
The Human Kernel 210 C #24
The Population Posterior and Bayesian Modeling on Streams 210 C #28
The Self-Normalized Estimator for Counterfactual Learning 210 C #59
Top-k Multiclass SVM 210 C #61
Training Restricted Boltzmann Machine via the ￼Thouless-Anderson-Palmer free energy 210 C #19
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models 210 C #42
Unlocking neural population non-stationarities using hierarchical dynamics models 210 C #21
Unsupervised Learning by Program Synthesis 210 C #15
Visalogy: Answering Visual Analogy Questions 210 C #5
Winner-Take-All Autoencoders 210 C #11

Accelerated Mirror Descent in Continuous and Discrete Time Room 210 A
Adaptive Online Learning Room 210 A
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Room 210 A
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Room 210 A
Information-theoretic lower bounds for convex optimization with erroneous oracles Room 210 A
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Room 210 A
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Room 210 A
Optimal Testing for Properties of Distributions Room 210 A

Attention-Based Models for Speech Recognition Room 210 A
Deep Convolutional Inverse Graphics Network Room 210 A
Learning to Segment Object Candidates Room 210 A
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Room 210 A
Spatial Transformer Networks Room 210 A
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Room 210 A
Training Very Deep Networks Room 210 A
Where are they looking? Room 210 A

A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Room 210 A
Action-Conditional Video Prediction using Deep Networks in Atari Games Room 210 A
Backpropagation for Energy-Efficient Neuromorphic Computing Room 210 A
Color Constancy by Learning to Predict Chromaticity from Luminance Room 210 A
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Room 210 A
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Room 210 A
Learning Wake-Sleep Recurrent Attention Models Room 210 A
On-the-Job Learning with Bayesian Decision Theory Room 210 A

Equilibrated adaptive learning rates for non-convex optimization Room 210 A
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Room 210 A
Pointer Networks Room 210 A
Precision-Recall-Gain Curves: PR Analysis Done Right Room 210 A
Structured Transforms for Small-Footprint Deep Learning Room 210 A

Accelerated Deep Learning on GPUs: From Large Scale Training to Embedded Deployment 210D
CodaLab Worksheets for Reproducible, Executable Papers 210D
Data-Driven Speech Animation 210D
Interactive Incremental Question Answering 210D
Scaling up visual search for product recommendation 210D
The pMMF multiresolution matrix factorization library 210D
A Complete Recipe for Stochastic Gradient MCMC 210 C #47
A Dual Augmented Block Minimization Framework for Learning with Limited Memory 210 C #83
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure 210 C #21
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice 210 C #86
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks 210 C #41
A Reduced-Dimension fMRI Shared Response Model 210 C #23
A Structural Smoothing Framework For Robust Graph Comparison 210 C #36
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding 210 C #26
A Universal Catalyst for First-Order Optimization 210 C #87
Accelerated Mirror Descent in Continuous and Discrete Time 210 C #96
Action-Conditional Video Prediction using Deep Networks in Atari Games 210 C #18
Adaptive Online Learning 210 C #100
Approximating Sparse PCA from Incomplete Data 210 C #62
Associative Memory via a Sparse Recovery Model 210 C #74
Attention-Based Models for Speech Recognition 210 C #5
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments 210 C #10
Backpropagation for Energy-Efficient Neuromorphic Computing 210 C #13
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff 210 C #98
Barrier Frank-Wolfe for Marginal Inference 210 C #48
Bayesian Active Model Selection with an Application to Automated Audiometry 210 C #19
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs 210 C #99
Bounding errors of Expectation-Propagation 210 C #70
Color Constancy by Learning to Predict Chromaticity from Luminance 210 C #16
Consistent Multilabel Classification 210 C #39
Convergence Rates of Active Learning for Maximum Likelihood Estimation 210 C #80
Convergence rates of sub-sampled Newton methods 210 C #77
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation 210 C #17
Deep Convolutional Inverse Graphics Network 210 C #6
Deep Visual Analogy-Making 210 C #1
Efficient and Robust Automated Machine Learning 210 C #20
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets 210 C #24
Efficient Non-greedy Optimization of Decision Trees 210 C #42
Efficient Output Kernel Learning for Multiple Tasks 210 C #54
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces 210 C #58
End-To-End Memory Networks 210 C #7
Equilibrated adaptive learning rates for non-convex optimization 210 C #27
Exactness of Approximate MAP Inference in Continuous MRFs 210 C #91
Fast and Memory Optimal Low-Rank Matrix Approximation 210 C #88
Finite-Time Analysis of Projected Langevin Monte Carlo 210 C #93
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial 210 C #32
Gaussian Process Random Fields 210 C #29
Gradient Estimation Using Stochastic Computation Graphs 210 C #55
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families 210 C #46
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality 210 C #73
Human Memory Search as Initial-Visit Emitting Random Walk 210 C #33
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets 210 C #9
Information-theoretic lower bounds for convex optimization with erroneous oracles 210 C #97
Inverse Reinforcement Learning with Locally Consistent Reward Functions 210 C #38
Is Approval Voting Optimal Given Approval Votes? 210 C #40
Kullback-Leibler Proximal Variational Inference 210 C #51
Learning Causal Graphs with Small Interventions 210 C #67
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring 210 C #52
Learning Theory and Algorithms for Forecasting Non-stationary Time Series 210 C #95
Learning to Segment Object Candidates 210 C #8
Learning Wake-Sleep Recurrent Attention Models 210 C #14
Lifted Inference Rules With Constraints 210 C #56
Linear Multi-Resource Allocation with Semi-Bandit Feedback 210 C #90
Local Smoothness in Variance Reduced Optimization 210 C #72
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents 210 C #71
Matrix Completion Under Monotonic Single Index Models 210 C #75
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets 210 C #65
MCMC for Variationally Sparse Gaussian Processes 210 C #30
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection 210 C #63
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning 210 C #28
Non-convex Statistical Optimization for Sparse Tensor Graphical Model 210 C #79
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations 210 C #69
On the consistency theory of high dimensional variable screening 210 C #92
On the Global Linear Convergence of Frank-Wolfe Optimization Variants 210 C #84
On-the-Job Learning with Bayesian Decision Theory 210 C #15
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach 210 C #60
Optimal Testing for Properties of Distributions 210 C #94
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference 210 C #37
Pointer Networks 210 C #22
Policy Gradient for Coherent Risk Measures 210 C #82
Practical and Optimal LSH for Angular Distance 210 C #49
Precision-Recall-Gain Curves: PR Analysis Done Right 210 C #25
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions 210 C #50
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling 210 C #85
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters 210 C #64
Regret-Based Pruning in Extensive-Form Games 210 C #68
Segregated Graphs and Marginals of Chain Graph Models 210 C #61
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding 210 C #11
Softstar: Heuristic-Guided Probabilistic Inference 210 C #45
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent 210 C #76
Sparse PCA via Bipartite Matchings 210 C #57
Spatial Transformer Networks 210 C #3
Spectral Learning of Large Structured HMMs for Comparative Epigenomics 210 C #35
Statistical Topological Data Analysis - A Kernel Perspective 210 C #43
Stochastic Online Greedy Learning with Semi-bandit Feedbacks 210 C #89
Streaming Min-max Hypergraph Partitioning 210 C #53
Streaming, Distributed Variational Inference for Bayesian Nonparametrics 210 C #31
Structured Transforms for Small-Footprint Deep Learning 210 C #34
Testing Closeness With Unequal Sized Samples 210 C #66
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors 210 C #12
Training Very Deep Networks 210 C #4
Variance Reduced Stochastic Gradient Descent with Neighbors 210 C #78
Variational Consensus Monte Carlo 210 C #44
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization 210 C #59
When are Kalman-Filter Restless Bandits Indexable? 210 C #81
Where are they looking? 210 C #2

Collaborative Filtering with Graph Information: Consistency and Scalable Methods Room 210 A
Efficient and Parsimonious Agnostic Active Learning Room 210 A
Fast and Guaranteed Tensor Decomposition via Sketching Room 210 A
Learning with Symmetric Label Noise: The Importance of Being Unhinged Room 210 A
Logarithmic Time Online Multiclass prediction Room 210 A
Matrix Completion with Noisy Side Information Room 210 A
Scalable Semi-Supervised Aggregation of Classifiers Room 210 A
Spherical Random Features for Polynomial Kernels Room 210 A

A fast, universal algorithm to learn parametric nonlinear embeddings 210 C #28
A Gaussian Process Model of Quasar Spectral Energy Distributions 210 C #16
A Market Framework for Eliciting Private Data 210 C #56
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA 210 C #50
Active Learning from Weak and Strong Labelers 210 C #46
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models 210 C #100
Adaptive Stochastic Optimization: From Sets to Paths 210 C #69
Algorithmic Stability and Uniform Generalization 210 C #84
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits 210 C #73
Analysis of Robust PCA via Local Incoherence 210 C #83
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care 210 C #96
Bayesian dark knowledge 210 C #21
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) 210 C #29
Bounding the Cost of Search-Based Lifted Inference 210 C #39
Collaborative Filtering with Graph Information: Consistency and Scalable Methods 210 C #62
Column Selection via Adaptive Sampling 210 C #76
Compressive spectral embedding: sidestepping the SVD 210 C #52
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting 210 C #11
Convolutional Networks on Graphs for Learning Molecular Fingerprints 210 C #10
Convolutional spike-triggered covariance analysis for neural subunit models 210 C #18
Copeland Dueling Bandits 210 C #88
Cornering Stationary and Restless Mixing Bandits with Remix-UCB 210 C #94
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling 210 C #34
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions 210 C #15
Differentially private subspace clustering 210 C #51
Discriminative Robust Transformation Learning 210 C #31
Efficient and Parsimonious Agnostic Active Learning 210 C #61
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors 210 C #86
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction 210 C #40
Efficient Thompson Sampling for Online ￼Matrix-Factorization Recommendation 210 C #43
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images 210 C #20
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture 210 C #23
Enforcing balance allows local supervised learning in spiking recurrent networks 210 C #4
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach 210 C #41
Fast and Guaranteed Tensor Decomposition via Sketching 210 C #47
Fast Classification Rates for High-dimensional Gaussian Generative Models 210 C #60
Fast Distributed k-Center Clustering with Outliers on Massive Data 210 C #58
Fast Lifted MAP Inference via Partitioning 210 C #45
Fast Rates for Exp-concave Empirical Risk Minimization 210 C #99
Fighting Bandits with a New Kind of Smoothness 210 C #95
From random walks to distances on unweighted graphs 210 C #74
Generalization in Adaptive Data Analysis and Holdout Reuse 210 C #53
GP Kernels for Cross-Spectrum Analysis 210 C #22
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems 210 C #93
Learnability of Influence in Networks 210 C #49
Learning structured densities via infinite dimensional exponential families 210 C #70
Learning to Linearize Under Uncertainty 210 C #7
Learning with Incremental Iterative Regularization 210 C #80
Learning with Symmetric Label Noise: The Importance of Being Unhinged 210 C #72
Less is More: Nyström Computational Regularization 210 C #63
Lifelong Learning with Non-i.i.d. Tasks 210 C #71
Local Causal Discovery of Direct Causes and Effects 210 C #30
Logarithmic Time Online Multiclass prediction 210 C #37
M-Best-Diverse Labelings for Submodular Energies and Beyond 210 C #33
Matrix Completion with Noisy Side Information 210 C #55
Max-Margin Deep Generative Models 210 C #14
Max-Margin Majority Voting for Learning from Crowds 210 C #32
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction 210 C #13
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path 210 C #85
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms 210 C #77
Natural Neural Networks 210 C #9
Neural Adaptive Sequential Monte Carlo 210 C #17
No-Regret Learning in Bayesian Games 210 C #81
On the Optimality of Classifier Chain for Multi-label Classification 210 C #65
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs 210 C #92
Online F-Measure Optimization 210 C #54
Online Learning for Adversaries with Memory: Price of Past Mistakes 210 C #90
Online Learning with Gaussian Payoffs and Side Observations 210 C #98
Optimal Linear Estimation under Unknown Nonlinear Transform 210 C #78
Optimal Ridge Detection using Coverage Risk 210 C #57
Orthogonal NMF through Subspace Exploration 210 C #59
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models 210 C #36
Parallelizing MCMC with Random Partition Trees 210 C #44
Particle Gibbs for Infinite Hidden Markov Models 210 C #24
Predtron: A Family of Online Algorithms for General Prediction Problems 210 C #64
Rectified Factor Networks 210 C #19
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring 210 C #89
Revenue Optimization against Strategic Buyers 210 C #91
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach 210 C #79
Robust Regression via Hard Thresholding 210 C #75
Robust Spectral Inference for Joint Stochastic Matrix Factorization 210 C #26
Saliency, Scale and Information: Towards a Unifying Theory 210 C #2
Sample Efficient Path Integral Control under Uncertainty 210 C #42
Scalable Semi-Supervised Aggregation of Classifiers 210 C #38
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks 210 C #12
Secure Multi-party Differential Privacy 210 C #68
Semi-supervised Learning with Ladder Networks 210 C #3
Semi-supervised Sequence Learning 210 C #5
Skip-Thought Vectors 210 C #6
Smooth Interactive Submodular Set Cover 210 C #66
Space-Time Local Embeddings 210 C #27
Sparse and Low-Rank Tensor Decomposition 210 C #82
Sparse Local Embeddings for Extreme Multi-label Classification 210 C #25
Spherical Random Features for Polynomial Kernels 210 C #48
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring 210 C #8
Teaching Machines to Read and Comprehend 210 C #1
The Pareto Regret Frontier for Bandits 210 C #97
Time-Sensitive Recommendation From Recurrent User Activities 210 C #35
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number 210 C #67
Unified View of Matrix Completion under General Structural Constraints 210 C #87

ABC in Montreal 511 a
Adaptive Data Analysis 514 a
Advances in Approximate Bayesian Inference 513 ab
Applying (machine) Learning to Experimental Physics (ALEPH) and «Flavours of Physics» challenge 515 bc
Bounded Optimality and Rational Metareasoning 512 bf
Cognitive Computation: Integrating neural and symbolic approaches 512 cg
Deep Reinforcement Learning 513 cd
Learning Faster from Easy Data II 511 d
Machine Learning and Interpretation in Neuroimaging (day 1) Room 515 a
Machine Learning for (e-)Commerce 512 e
Machine Learning For Healthcare (MLHC) 510 db
Machine Learning for Spoken Language Understanding and Interactions 511 b
Modelling and inference for dynamics on complex interaction networks: joining up machine learning and statistical physics 511 e
Multimodal Machine Learning 512 dh
Nonparametric Methods for Large Scale Representation Learning 511 c
Optimization for Machine Learning (OPT2015) 510 ac
Probabilistic Integration 512 a
Statistical Methods for Understanding Neural Systems 511 f
The 1st International Workshop 'Feature Extraction: Modern Questions and Challenges' 513 ef
Time Series Workshop 514 bc

Bayesian Nonparametrics: The Next Generation 515 bc
Bayesian Optimization: Scalability and Flexibility 511 b
BigNeuro 2015: Making sense of big neural data 511 e
Black box learning and inference 513 ef
Challenges in Machine Learning (CiML 2015): 'Open Innovation' and 'Coopetitions' 512 e
Learning and privacy with incomplete data and weak supervision 512 dh
Learning, Inference and Control of Multi-Agent Systems 511 a
Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization 514 a
Machine Learning in Computational Biology 510 db
Machine Learning Systems 511 d
Multiresolution methods for large-scale learning 511 c
Networks in the Social and Information Sciences 512 bf
Non-convex Optimization for Machine Learning: Theory and Practice 513 cd
Quantum Machine Learning 512 a
Reasoning, Attention, Memory (RAM) Workshop 510 ac
Scalable Monte Carlo Methods for Bayesian Analysis of Big Data 513 ab
Transfer and Multi-Task Learning: Trends and New Perspectives 514 bc