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Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
Learning with Symmetric Label Noise: The Importance of Being Unhinged
Algorithmic Stability and Uniform Generalization
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
Robust Portfolio Optimization
Logarithmic Time Online Multiclass prediction
Planar Ultrametrics for Image Segmentation
Expressing an Image Stream with a Sequence of Natural Sentences
Parallel Correlation Clustering on Big Graphs
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Space-Time Local Embeddings
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Smooth Interactive Submodular Set Cover
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
On the Pseudo-Dimension of Nearly Optimal Auctions
Unlocking neural population non-stationarities using hierarchical dynamics models
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Color Constancy by Learning to Predict Chromaticity from Luminance
Fast and Accurate Inference of Plackett–Luce Models
Probabilistic Line Searches for Stochastic Optimization
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Where are they looking?
The Pareto Regret Frontier for Bandits
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
Measuring Sample Quality with Stein's Method
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
Bounding errors of Expectation-Propagation
A fast, universal algorithm to learn parametric nonlinear embeddings
Texture Synthesis Using Convolutional Neural Networks
Extending Gossip Algorithms to Distributed Estimation of U-statistics
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
Learning visual biases from human imagination
Smooth and Strong: MAP Inference with Linear Convergence
Copeland Dueling Bandits
Optimal Ridge Detection using Coverage Risk
Top-k Multiclass SVM
Policy Evaluation Using the Ω-Return
Orthogonal NMF through Subspace Exploration
Stochastic Online Greedy Learning with Semi-bandit Feedbacks
Deeply Learning the Messages in Message Passing Inference
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
Accelerated Proximal Gradient Methods for Nonconvex Programming
Approximating Sparse PCA from Incomplete Data
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
Column Selection via Adaptive Sampling
HONOR: Hybrid Optimization for NOn-convex Regularized problems
3D Object Proposals for Accurate Object Class Detection
Algorithms with Logarithmic or Sublinear Regret for  Constrained Contextual Bandits
Tensorizing Neural Networks
Parallelizing MCMC with Random Partition Trees
A Reduced-Dimension fMRI Shared Response Model
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
Estimating Mixture Models via Mixtures of Polynomials
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Deep Knowledge Tracing
Rethinking LDA: Moment Matching for Discrete ICA
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
Barrier Frank-Wolfe for Marginal Inference
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Compressive spectral embedding: sidestepping the SVD
A Nonconvex Optimization Framework for Low Rank Matrix Estimation
Automatic Variational Inference in Stan
Attention-Based Models for Speech Recognition
Closed-form Estimators for High-dimensional Generalized Linear Models
Online F-Measure Optimization
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
M-Best-Diverse Labelings for Submodular Energies and Beyond
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy
Character-level Convolutional Networks for Text Classification
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis
Black-box optimization of noisy functions with unknown smoothness
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters
Deep learning with Elastic Averaging SGD
Monotone k-Submodular Function Maximization with Size Constraints
Active Learning from Weak and Strong Labelers
On the Optimality of Classifier Chain for Multi-label Classification
Robust Regression via Hard Thresholding
Sparse Local Embeddings for Extreme Multi-label Classification
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
Sparse PCA via Bipartite Matchings
Fast Randomized Kernel Ridge Regression with Statistical Guarantees
Online Learning for Adversaries with Memory: Price of Past Mistakes
Convolutional spike-triggered covariance analysis for neural subunit models
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
GAP Safe screening rules for sparse multi-task and multi-class models
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
Statistical Model Criticism using Kernel Two Sample Tests
Precision-Recall-Gain Curves: PR Analysis Done Right
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
Bidirectional Recurrent Neural Networks as Generative Models
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
Large-scale probabilistic predictors with and without guarantees of validity
Shepard Convolutional Neural Networks
Matrix Manifold Optimization for Gaussian Mixtures
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling
Bounding the Cost of Search-Based Lifted Inference
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Linear Multi-Resource Allocation with Semi-Bandit Feedback
Unsupervised Learning by Program Synthesis
Enforcing balance allows local supervised learning in spiking recurrent networks
Fast and Guaranteed Tensor Decomposition via Sketching
Differentially private subspace clustering
Predtron: A Family of Online Algorithms for General Prediction Problems
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
Fast Classification Rates for High-dimensional Gaussian Generative Models
Fast Distributed k-Center Clustering with Outliers on Massive Data
Human Memory Search as Initial-Visit Emitting Random Walk
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Convergence Rates of Active Learning for Maximum Likelihood Estimation
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
Backpropagation for Energy-Efficient Neuromorphic Computing
Alternating Minimization for Regression Problems with Vector-valued Outputs
Learning both Weights and Connections for Efficient Neural Network
Optimal Rates for Random Fourier Features
The Population Posterior and Bayesian Modeling on Streams
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Unified View of Matrix Completion under General Structural Constraints
Efficient Output Kernel Learning for Multiple Tasks
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
Variational Consensus Monte Carlo
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma
Practical and Optimal LSH for Angular Distance
Learning to Linearize Under Uncertainty
Finite-Time Analysis of Projected Langevin Monte Carlo
Deep Visual Analogy-Making
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
Online Learning with Adversarial Delays
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
Minimum Weight Perfect Matching via Blossom Belief Propagation
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems
Lifted Symmetry Detection and Breaking for MAP Inference
Evaluating the statistical significance of biclusters
Discriminative Robust Transformation Learning
Bandits with Unobserved Confounders: A Causal Approach
Scalable Semi-Supervised Aggregation of Classifiers
Online Learning with Gaussian Payoffs and Side Observations
Private Graphon Estimation for Sparse Graphs
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
Fast Second Order Stochastic Backpropagation for Variational Inference
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
Fast Bidirectional Probability Estimation in Markov Models
Probabilistic Variational Bounds for Graphical Models
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Combinatorial Cascading Bandits
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
Policy Gradient for Coherent Risk Measures
Fast Rates for Exp-concave Empirical Risk Minimization
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Equilibrated adaptive learning rates for non-convex optimization
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care
Lifelong Learning with Non-i.i.d. Tasks
Optimal Linear Estimation under Unknown Nonlinear Transform
Learning with Group Invariant Features: A Kernel Perspective.
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Adaptive Stochastic Optimization: From Sets to Paths
Beyond Convexity: Stochastic Quasi-Convex Optimization
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
Sum-of-Squares Lower Bounds for Sparse PCA
Max-Margin Majority Voting for Learning from Crowds
Learning with Incremental Iterative Regularization
Halting in Random Walk Kernels
MCMC for Variationally Sparse Gaussian Processes
Less is More: Nyström Computational Regularization
Infinite Factorial Dynamical Model
Regularization Path of Cross-Validation Error Lower Bounds
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments
Teaching Machines to Read and Comprehend
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
When are Kalman-Filter Restless Bandits Indexable?
Segregated Graphs and Marginals of Chain Graph Models
Efficient Non-greedy Optimization of Decision Trees
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Inverse Reinforcement Learning with Locally Consistent Reward Functions
Communication Complexity of Distributed Convex Learning and Optimization
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Subset Selection by Pareto Optimization
On the Accuracy of Self-Normalized Log-Linear Models
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
Is Approval Voting Optimal Given Approval Votes?
Regressive Virtual Metric Learning
Analysis of Robust PCA via Local Incoherence
Learning to Transduce with Unbounded Memory
Max-Margin Deep Generative Models
Spherical Random Features for Polynomial Kernels
Rectified Factor Networks
Learning Bayesian Networks with Thousands of Variables
Matrix Completion Under Monotonic Single Index Models
Visalogy: Answering Visual Analogy Questions
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Streaming Min-max Hypergraph Partitioning
Collaboratively Learning Preferences from Ordinal Data
Biologically Inspired Dynamic Textures for Probing Motion Perception
Generative Image Modeling Using Spatial LSTMs
Robust PCA with compressed data
Sampling from Probabilistic Submodular Models
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Supervised Learning for Dynamical System Learning
Regret-Based Pruning in Extensive-Form Games
Fast Two-Sample Testing with Analytic Representations of Probability Measures
Learning to Segment Object Candidates
GP Kernels for Cross-Spectrum Analysis
Secure Multi-party Differential Privacy
Spatial Transformer Networks
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
High-dimensional neural spike train analysis with generalized count linear dynamical systems
Learning with a Wasserstein Loss
b-bit Marginal Regression
Natural Neural Networks
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing
On some provably correct cases of variational inference for topic models
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
Combinatorial Bandits Revisited
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
A Structural Smoothing Framework For Robust Graph Comparison
Competitive Distribution Estimation: Why is Good-Turing Good
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
A hybrid sampler for Poisson-Kingman mixture models
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching
Local Smoothness in Variance Reduced Optimization
Saliency, Scale and Information: Towards a Unifying Theory
Fighting Bandits with a New Kind of Smoothness
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
Tractable Learning for Complex Probability Queries
StopWasting My Gradients: Practical SVRG
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Learning structured densities via infinite dimensional exponential families
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
Variance Reduced Stochastic Gradient Descent with Neighbors
Sample Efficient Path Integral Control under Uncertainty
Stochastic Expectation Propagation
Exactness of Approximate MAP Inference in Continuous MRFs
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
Generalization in Adaptive Data Analysis and Holdout Reuse
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
Training Very Deep Networks
Bayesian Active Model Selection with an Application to Automated Audiometry
Particle Gibbs for Infinite Hidden Markov Models
Learning spatiotemporal trajectories from manifold-valued longitudinal data
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
On the consistency theory of high dimensional variable screening
End-To-End Memory Networks
Spectral Representations for Convolutional Neural Networks
Online Gradient Boosting
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Recognizing retinal ganglion cells in the dark
A Theory of Decision Making Under Dynamic Context
A Gaussian Process Model of Quasar Spectral Energy Distributions
Hidden Technical Debt in Machine Learning Systems
Local Causal Discovery of Direct Causes and Effects
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Revenue Optimization against Strategic Buyers
Deep Convolutional Inverse Graphics Network
Sparse and Low-Rank Tensor Decomposition
Minimax Time Series Prediction
Differentially Private Learning of Structured Discrete Distributions
Sample Complexity of Learning Mahalanobis Distance Metrics
Learning Wake-Sleep Recurrent Attention Models
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Testing Closeness With Unequal Sized Samples
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach
Neural Adaptive Sequential Monte Carlo
Local Expectation Gradients for Black Box Variational Inference
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
Super-Resolution Off the Grid
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors
Pointer Networks
Associative Memory via a Sparse Recovery Model
Robust Spectral Inference for Joint Stochastic Matrix Factorization
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
Adversarial Prediction Games for Multivariate Losses
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Efficient and Parsimonious Agnostic Active Learning
Softstar: Heuristic-Guided Probabilistic Inference
Grammar as a Foreign Language
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices
Winner-Take-All Autoencoders
Deep Poisson Factor Modeling
Bayesian Optimization with Exponential Convergence
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Learning with Relaxed Supervision
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's
Accelerated Mirror Descent in Continuous and Discrete Time
The Human Kernel
Action-Conditional Video Prediction using Deep Networks in Atari Games
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
Distributed Submodular Cover: Succinctly Summarizing Massive Data
Community Detection via Measure Space Embedding
Basis refinement strategies for linear value function approximation in MDPs
Structured Estimation with Atomic Norms: General Bounds and Applications
A Complete Recipe for Stochastic Gradient MCMC
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
Online Prediction at the Limit of Zero Temperature
Learning Continuous Control Policies by Stochastic Value Gradients
Exploring Models and Data for Image Question Answering
Efficient and Robust Automated Machine Learning
Preconditioned Spectral Descent for Deep Learning
A Recurrent Latent Variable Model for Sequential Data
Fast Convergence of Regularized Learning in Games
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
Reflection, Refraction, and Hamiltonian Monte Carlo
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels
Nearly Optimal Private LASSO
Convergence Analysis of Prediction Markets via Randomized Subspace Descent
The Poisson Gamma Belief Network
Convergence rates of sub-sampled Newton methods
No-Regret Learning in Bayesian Games
Statistical Topological Data Analysis - A Kernel Perspective
Semi-supervised Sequence Learning
Structured Transforms for Small-Footprint Deep Learning
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Sample Complexity Bounds for Iterative Stochastic Policy Optimization
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Interactive Control of Diverse Complex Characters with Neural Networks
Submodular Hamming Metrics
A Universal Primal-Dual Convex Optimization Framework
Learning From Small Samples: An Analysis of Simple Decision Heuristics
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Fast and Memory Optimal Low-Rank Matrix Approximation
Learnability of Influence in Networks
Learning Causal Graphs with Small Interventions
Information-theoretic lower bounds for convex optimization with erroneous oracles
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
The Self-Normalized Estimator for Counterfactual Learning
Fast Lifted MAP Inference via Partitioning
Data Generation as Sequential Decision Making
On Elicitation Complexity
Decomposition Bounds for Marginal MAP
Discrete Rényi Classifiers
A class of network models recoverable by spectral clustering
Skip-Thought Vectors
Rate-Agnostic (Causal) Structure Learning
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric
Consistent Multilabel Classification
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
Cornering Stationary and Restless Mixing Bandits with Remix-UCB
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Gaussian Process Random Fields
M-Statistic for Kernel Change-Point Detection
Adaptive Online Learning
A Universal Catalyst for First-Order Optimization
Inference for determinantal point processes without spectral knowledge
Kullback-Leibler Proximal Variational Inference
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
From random walks to distances on unweighted graphs
Bayesian dark knowledge
Matrix Completion with Noisy Side Information
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
On-the-Job Learning with Bayesian Decision Theory
Calibrated Structured Prediction
Learning Structured Output Representation using Deep Conditional Generative Models
Time-Sensitive Recommendation From Recurrent User Activities
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
A Market Framework for Eliciting Private Data
Lifted Inference Rules With Constraints
Gradient Estimation Using Stochastic Computation Graphs
Model-Based Relative Entropy Stochastic Search
Semi-supervised Learning with Ladder Networks
Embedding Inference for Structured Multilabel Prediction
Copula variational inference
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
A Dual Augmented Block Minimization Framework for Learning with Limited Memory
Optimal Testing for Properties of Distributions
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
Expectation Particle Belief Propagation
Latent Bayesian melding for integrating individual and population models
Distributionally Robust Logistic Regression
Variational Dropout and the Local Reparameterization Trick