statistical learning

On the Concentration of the Minimizers of Empirical Risks JMLR 2024 On the sample complexity of parameter estimation in logistic regression with normal design COLT 2024 Optimal Sample Complexity of Contrastive Learning ICLR 2024 PAC-Bayes-Chernoff bounds for unbounded losses NeurIPS 2024 Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models ICLR 2024 Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity NeurIPS 2024 Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds ICLR 2024 Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution NeurIPS 2024 Robust Sparse Regression with Non-Isotropic Designs NeurIPS 2024 Safe and Interpretable Estimation of Optimal Treatment Regimes AISTATS 2024 Stability and L2-penalty in Model Averaging JMLR 2024 Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NeurIPS 2024 Statistical curriculum learning: An elimination algorithm achieving an oracle risk COLT 2024 The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems NeurIPS 2024 The Sample Complexity of Simple Binary Hypothesis Testing COLT 2024 Towards Exact Computation of Inductive Bias IJCAI 2024 Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression ICLR 2024 A Fair Generative Model Using LeCam Divergence AAAI 2023 A Generalized Unbiased Risk Estimator for Learning with Augmented Classes AAAI 2023 Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions NeurIPS 2023 An Ordinal Latent Variable Model of Conflict Intensity ACL 2023 A Statistical Learning Take on the Concordance Index for Survival Analysis AISTATS 2023 A Theory of Multimodal Learning NeurIPS 2023 A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning NeurIPS 2023 Average case analysis of Lasso under ultra sparse conditions AISTATS 2023