vc dimension

Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness COLT 2026 Group-realizable multi-group learning by minimizing empirical risk ALT 2026 Invited Open Problem: Does Differential Privacy Make PAC Learning Much Harder? COLT 2026 Learning with Simulators: No Regret in a Computationally Bounded World COLT 2026 Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning COLT 2026 Tight Sample Complexity of Transformers COLT 2026 Uniform Laws of Large Numbers in Product Spaces COLT 2026 A Theory of Learning with Autoregressive Chain of Thought COLT 2025 Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks COLT 2025 Computing High-dimensional Confidence Sets for Arbitrary Distributions COLT 2025 Lower Bounds for Greedy Teaching Set Constructions COLT 2025 No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models ICLR 2025 Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity COLT 2025 Proofs as Explanations: Short Certificates for Reliable Predictions COLT 2025 Sample Compression Scheme Reductions ALT 2025 Simplifying Adversarially Robust PAC Learning With Tolerance COLT 2025 Spherical Dimension COLT 2025 The Space Complexity of Learning-Unlearning Algorithms (extended abstract) COLT 2025 Towards Fundamental Limits for Active Multi-distribution Learning COLT 2025 Active Learning with Simple Questions COLT 2024 A Theory of Fault-Tolerant Learning ICML 2024 A Theory of Interpretable Approximations COLT 2024 Collaborative Learning with Different Labeling Functions ICML 2024 Derandomizing Multi-Distribution Learning NeurIPS 2024 Efficient Algorithms for Learning Monophonic Halfspaces in Graphs COLT 2024