Statistical Learning

How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? AAAI 2019 Human Uncertainty Makes Classification More Robust ICCV 2019 Hypothesis Set Stability and Generalization NeurIPS 2019 Implicit Regularization for Optimal Sparse Recovery NeurIPS 2019 Importance Sampling Policy Evaluation with an Estimated Behavior Policy ICML 2019 Improving Model Selection by Employing the Test Data ICML 2019 Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization AISTATS 2019 Inference under Information Constraints: Lower Bounds from Chi-Square Contraction COLT 2019 Interplay of minimax estimation and minimax support recovery under sparsity ALT 2019 Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning NeurIPS 2019 Joint Nonparametric Precision Matrix Estimation with Confounding UAI 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019 Kernel Stein Tests for Multiple Model Comparison NeurIPS 2019 Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NeurIPS 2019 Landmark Ordinal Embedding NeurIPS 2019 Latent Semantics Encoding for Label Distribution Learning IJCAI 2019 Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models JMLR 2019 Learnability of Solutions to Conjunctive Queries JMLR 2019 Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models JMLR 2019 Learning Distributions Generated by One-Layer ReLU Networks NeurIPS 2019 Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019 Learning Mixtures of Plackett-Luce Models from Structured Partial Orders NeurIPS 2019 Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm AISTATS 2019 Learning Nonsymmetric Determinantal Point Processes NeurIPS 2019 Learning Tree Structures from Noisy Data AISTATS 2019