Generalization for Adaptively-chosen Estimators via Stable Median ( abstract )

Learning Non-Discriminatory Predictors ( abstract )

The Price of Selection in Differential Privacy ( abstract )

Efficient PAC Learning from the Crowd ( abstract )

A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics (Best Paper Award) ( abstract )

Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A Nonasymptotic Analysis ( abstract )

Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent ( abstract )

Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo ( abstract )

Fast Rates for Empirical Risk Minimization of Strict Saddle Problems ( abstract )

Sample complexity of population recovery ( abstract )

Noisy Population Recovery from Unknown Noise ( abstract )

Learning Multivariate Log-concave Distributions ( abstract )

Ten Steps of EM Suffice for Mixtures of Two Gaussians ( abstract )

The Hidden Hubs Problem ( abstract )

Sparse Stochastic Bandits ( abstract )

An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits ( abstract )

Corralling a Band of Bandit Algorithms ( abstract )

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization ( abstract )

Towards Instance Optimal Bounds for Best Arm Identification ( abstract )

Bandits with Movement Costs and Adaptive Pricing ( abstract )

Tight Bounds for Bandit Combinatorial Optimization ( abstract )

Online Nonparametric Learning, Chaining, and the Role of Partial Feedback ( abstract )

Open Problem: First-Order Regret Bounds for Contextual Bandits ( abstract )

Open Problem: Meeting Times for Learning Random Automata ( abstract )

Adaptivity to Noise Parameters in Nonparametric Active Learning ( abstract )

Computationally Efficient Robust Estimation of Sparse Functionals ( abstract )

Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities ( abstract )

Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization ( abstract )

Thresholding based Efficient Outlier Robust PCA ( abstract )

Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality ( abstract )

Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems ( abstract )

Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization ( abstract )

Submodular Optimization under Noise ( abstract )

Correspondence retrieval ( abstract )

Online Learning Without Prior Information (Best Student Paper Award) ( abstract )

On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities ( abstract )

Fast rates for online learning in Linearly Solvable Markov Decision Processes ( abstract )

ZIGZAG: A new approach to adaptive online learning ( abstract )

Efficient Co-Training of Linear Separators under Weak Dependence ( abstract )

Effective Semisupervised Learning on Manifolds ( abstract )

Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes ( abstract )

Learning Disjunctions of Predicates ( abstract )

A General Characterization of the Statistical Query Complexity ( abstract )

Mixing Implies Lower Bounds for Space Bounded Learning ( abstract )

On Learning versus Refutation ( abstract )

Inapproximability of VC Dimension and Littlestone's Dimension ( abstract )

Memoryless Sequences for Differentiable Losses ( abstract )

Multi-Observation Elicitation ( abstract )

Testing Bayesian Networks ( abstract )

Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing ( abstract )

Two-Sample Tests for Large Random Graphs using Network Statistics ( abstract )

Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds ( abstract )

Stochastic Composite Least-Squares Regression with convergence rate O(1/n) ( abstract )

A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints ( abstract )

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox ( abstract )

The Sample Complexity of Optimizing a Convex Function ( abstract )

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime ( abstract )

Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons ( abstract )

Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration ( abstract )

Thompson Sampling for the MNL-Bandit ( abstract )

On the Ability of Neural Nets to Express Distributions ( abstract )

Depth Separation for Neural Networks ( abstract )

Surprising properties of dropout in deep networks ( abstract )

Reliably Learning the ReLU in Polynomial Time ( abstract )

Nearly-tight VC-dimension bounds for neural networks ( abstract )

Exact tensor completion with sum-of-squares ( abstract )

Fast and robust tensor decomposition with applications to dictionary learning ( abstract )

Homotopy Analysis for Tensor PCA ( abstract )

Fundamental limits of symmetric low-rank matrix estimation ( abstract )

Matrix Completion from O(n) Samples in Linear Time ( abstract )

High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition. ( abstract )

Rates of estimation for determinantal point processes ( abstract )

Predicting with Distributions ( abstract )

A second-order look at stability and generalization ( abstract )

Optimal learning via local entropies and sample compression ( abstract )