pac learning

Private learning implies quantum stability NeurIPS 2021 Sample-Optimal PAC Learning of Halfspaces with Malicious Noise ICML 2021 Sequential prediction under log-loss and misspecification COLT 2021 Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples JMLR 2021 Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound ALT 2021 The Sample Complexity of Teaching by Reinforcement on Q-Learning AAAI 2021 Weak learning convex sets under normal distributions COLT 2021 A Computational Separation between Private Learning and Online Learning NeurIPS 2020 Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks COLT 2020 An Optimal Elimination Algorithm for Learning a Best Arm NeurIPS 2020 Best-item Learning in Random Utility Models with Subset Choices AISTATS 2020 Black-box Certification and Learning under Adversarial Perturbations ICML 2020 Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems AAAI 2020 Budget Learning via Bracketing AISTATS 2020 Closure Properties for Private Classification and Online Prediction COLT 2020 Efficient, Noise-Tolerant, and Private Learning via Boosting COLT 2020 Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning COLT 2020 From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model ICML 2020 Learning from Mixtures of Private and Public Populations NeurIPS 2020 Learning the Valuations of a k-demand Agent ICML 2020 Metric-Free Individual Fairness in Online Learning NeurIPS 2020 Non-Convex SGD Learns Halfspaces with Adversarial Label Noise NeurIPS 2020 On Learnability wih Computable Learners ALT 2020 On the Equivalence between Online and Private Learnability beyond Binary Classification NeurIPS 2020 On the Learnability of Possibilistic Theories IJCAI 2020