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Type: Posters [clear filter]
Monday, December 7


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
Tuesday, December 8


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
Wednesday, December 9


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
Thursday, December 10


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