Learning Theory

Improved Robust Algorithms for Learning with Discriminative Feature Feedback AISTATS 2023 Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning AISTATS 2023 Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization CVPR 2023 Improving the Knowledge Gradient Algorithm NeurIPS 2023 Incentive-aware Contextual Pricing with Non-parametric Market Noise AISTATS 2023 Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training NeurIPS 2023 Inductive Bias Is in the Eye of the Beholder EMNLP 2023 Infinite Action Contextual Bandits with Reusable Data Exhaust ICML 2023 Influence Scores at Scale for Efficient Language Data Sampling EMNLP 2023 Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise COLT 2023 Information-Directed Selection for Top-Two Algorithms COLT 2023 Information-Theoretic Analysis of Unsupervised Domain Adaptation ICLR 2023 Information Transfer in Multitask Learning, Data Augmentation, and Beyond AAAI 2023 Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks NeurIPS 2023 Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks NeurIPS 2023 Instance-Dependent Generalization Bounds via Optimal Transport JMLR 2023 Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games AISTATS 2023 Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory COLT 2023 Interpolating Classifiers Make Few Mistakes JMLR 2023 Intractability of Learning the Discrete Logarithm with Gradient-Based Methods ACML 2023 Invariant Learning via Probability of Sufficient and Necessary Causes NeurIPS 2023 Inverse Scaling Can Become U-Shaped EMNLP 2023 Is interpolation benign for random forest regression? AISTATS 2023 Is Learning in Games Good for the Learners? NeurIPS 2023 Is Planted Coloring Easier than Planted Clique? COLT 2023