learning theory

Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning NeurIPS 2024 Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? NeurIPS 2024 Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL NeurIPS 2024 Language Generation in the Limit NeurIPS 2024 Learning from Snapshots of Discrete and Continuous Data Streams NeurIPS 2024 Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs NeurIPS 2024 Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains AAAI 2024 Majority-of-Three: The Simplest Optimal Learner? COLT 2024 Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input NeurIPS 2024 Memorization With Neural Nets: Going Beyond the Worst Case JMLR 2024 Model Collapse Demystified: The Case of Regression NeurIPS 2024 More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity JMLR 2024 Multiclass Transductive Online Learning NeurIPS 2024 Not All Learnable Distribution Classes are Privately Learnable ALT 2024 Omnipredictors for regression and the approximate rank of convex functions COLT 2024 On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability NeurIPS 2024 On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries NeurIPS 2024 On the Concentration of the Minimizers of Empirical Risks JMLR 2024 On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective AISTATS 2024 On the Efficiency of ERM in Feature Learning NeurIPS 2024 On the Generalization of Stochastic Gradient Descent with Momentum JMLR 2024 On the hardness of learning under symmetries ICLR 2024 On the Learnability of Out-of-distribution Detection JMLR 2024 On the sample complexity of privately learning half-spaces ACML 2024 On the Sparsity of the Strong Lottery Ticket Hypothesis NeurIPS 2024