Statistical Learning

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems NeurIPS 2022 Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison UAI 2022 Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference NeurIPS 2022 Random Graph Matching in Geometric Models: the Case of Complete Graphs COLT 2022 Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms COLT 2022 Realizable Learning is All You Need COLT 2022 Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise AISTATS 2022 Recovering shared structure from multiple networks with unknown edge distributions JMLR 2022 Recovering the Propensity Score from Biased Positive Unlabeled Data AAAI 2022 REGroup: Rank-Aggregating Ensemble of Generative Classifiers for Robust Predictions WACV 2022 Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability AAAI 2022 Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment AAAI 2022 Reproducibility Signals in Science: A preliminary analysis AACL 2022 Rethinking and Refining the Distinct Metric ACL 2022 Rethinking Class-Prior Estimation for Positive-Unlabeled Learning ICLR 2022 Rethinking Fano’s Inequality in Ensemble Learning ICML 2022 ReVar: Strengthening policy evaluation via reduced variance sampling UAI 2022 Revisiting Statistical Laws of Semantic Shift in Romance Cognates COLING 2022 Ridgeless Regression with Random Features IJCAI 2022 Risk bounds for aggregated shallow neural networks using Gaussian priors COLT 2022 Robust Bayesian Regression via Hard Thresholding NeurIPS 2022 Robust Direct Learning for Causal Data Fusion ACML 2022 Robust Estimation for Random Graphs COLT 2022 Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees ICML 2022 Robust Generalized Method of Moments: A Finite Sample Viewpoint NeurIPS 2022