Statistical Learning

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice ICML 2023 Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group AISTATS 2023 Replicable Clustering NeurIPS 2023 Reproducibility of the Methods in Medical Imaging with Deep Learning. MIDL 2023 Rethinking Backdoor Attacks ICML 2023 Rethinking Translation Memory Augmented Neural Machine Translation ACL 2023 Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge ICML 2023 Revisiting Discriminative vs. Generative Classifiers: Theory and Implications ICML 2023 Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare AISTATS 2023 Revisiting inference after prediction JMLR 2023 Revisiting minimum description length complexity in overparameterized models JMLR 2023 Revisit PCA-based Technique for Out-of-Distribution Detection ICCV 2023 Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws AISTATS 2023 Risk-aware linear bandits with convex loss AISTATS 2023 Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption JMLR 2023 Risk Bounds on Aleatoric Uncertainty Recovery AISTATS 2023 Robust and Agnostic Learning of Conditional Distributional Treatment Effects AISTATS 2023 Robust Causal Graph Representation Learning against Confounding Effects AAAI 2023 Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues ICML 2023 Robust Data Valuation with Weighted Banzhaf Values NeurIPS 2023 Robust Empirical Risk Minimization with Tolerance ALT 2023 Robust Estimation of Discrete Distributions under Local Differential Privacy ALT 2023 Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning JMLR 2023 Robust Mean Estimation Without Moments for Symmetric Distributions NeurIPS 2023 Robust Methods for High-Dimensional Linear Learning JMLR 2023