Statistical Learning

Learning Strategies in Decentralized Matching Markets under Uncertain Preferences JMLR 2021 Learning the optimal Tikhonov regularizer for inverse problems NeurIPS 2021 Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes JMLR 2021 Learning with invariances in random features and kernel models COLT 2021 Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs AAAI 2021 Learning with User-Level Privacy NeurIPS 2021 Least Square Calibration for Peer Reviews NeurIPS 2021 List-Decodable Mean Estimation in Nearly-PCA Time NeurIPS 2021 Locality defeats the curse of dimensionality in convolutional teacher-student scenarios NeurIPS 2021 Localization, Convexity, and Star Aggregation NeurIPS 2021 Locally Differentially-Private Randomized Response for Discrete Distribution Learning JMLR 2021 Loop Estimator for Discounted Values in Markov Reward Processes AAAI 2021 Loss function based second-order Jensen inequality and its application to particle variational inference NeurIPS 2021 Machine Learning for Variance Reduction in Online Experiments NeurIPS 2021 Majorizing Measures, Sequential Complexities, and Online Learning COLT 2021 MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers NeurIPS 2021 Maximum Likelihood Training of Score-Based Diffusion Models NeurIPS 2021 Maximum Mean Discrepancy Test is Aware of Adversarial Attacks ICML 2021 Measuring Dependence with Matrix-based Entropy Functional AAAI 2021 Measuring Generalization with Optimal Transport NeurIPS 2021 Metadata Normalization CVPR 2021 Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation ICML 2021 Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models JMLR 2021 Method of Moments for Topic Models with Mixed Discrete and Continuous Features IJCAI 2021 Minimax Estimation of Laplacian Constrained Precision Matrices AISTATS 2021