pac learning

On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences NeurIPS 2023 Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes COLT 2023 Optimal Learners for Realizable Regression: PAC Learning and Online Learning NeurIPS 2023 PAC Generalization via Invariant Representations ICML 2023 PAC Learning and Stabilizing Hedonic Games: Towards a Unifying Approach. AAAI 2023 PAC-learning for Strategic Classification JMLR 2023 PAC Learning Linear Thresholds from Label Proportions NeurIPS 2023 PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time AISTATS 2023 Private Everlasting Prediction NeurIPS 2023 Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints JMLR 2023 SAT-Based PAC Learning of Description Logic Concepts IJCAI 2023 Semi-Verified PAC Learning from the Crowd AISTATS 2023 Strategic Classification under Unknown Personalized Manipulation NeurIPS 2023 The Learnability of In-Context Learning NeurIPS 2023 The One-Inclusion Graph Algorithm is not Always Optimal COLT 2023 Towards Theoretical Understanding of Inverse Reinforcement Learning ICML 2023 TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models ICCV 2023 Tree Learning: Optimal Sample Complexity and Algorithms AAAI 2023 User-Level Differential Privacy With Few Examples Per User NeurIPS 2023 When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS 2023 A Characterization of Semi-Supervised Adversarially Robust PAC Learnability NeurIPS 2022 Active learning with label comparisons UAI 2022 Adaptive Multi-Goal Exploration AISTATS 2022 A Fast Algorithm for PAC Combinatorial Pure Exploration AAAI 2022 Beyond No Regret: Instance-Dependent PAC Reinforcement Learning COLT 2022