pac learning

On the Sample Complexity of Adversarial Multi-Source PAC Learning ICML 2020 On the Sample Complexity of Learning Sum-Product Networks AISTATS 2020 PAC learning with stable and private predictions COLT 2020 Probably Approximately Correct Constrained Learning NeurIPS 2020 Proper Learning, Helly Number, and an Optimal SVM Bound COLT 2020 Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration NeurIPS 2020 Recovery of sparse linear classifiers from mixture of responses NeurIPS 2020 Reducing Adversarially Robust Learning to Non-Robust PAC Learning NeurIPS 2020 Sequential Mode Estimation with Oracle Queries AAAI 2020 Synthetic Data Generators -- Sequential and Private NeurIPS 2020 The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise NeurIPS 2020 The Power of Comparisons for Actively Learning Linear Classifiers NeurIPS 2020 The Sample Complexity of Best-k Items Selection from Pairwise Comparisons ICML 2020 The True Sample Complexity of Identifying Good Arms AISTATS 2020 Active Ranking with Subset-wise Preferences AISTATS 2019 An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory JMLR 2019 A PAC Framework for Aggregating Agents’ Judgments AAAI 2019 Can SGD Learn Recurrent Neural Networks with Provable Generalization? NeurIPS 2019 Cautious Rule-Based Collective Inference IJCAI 2019 Characterizing the Sample Complexity of Pure Private Learners JMLR 2019 Differentially Private Learning of Geometric Concepts ICML 2019 Dimension-Free Error Bounds from Random Projections AAAI 2019 Distribution-Independent PAC Learning of Halfspaces with Massart Noise NeurIPS 2019 Efficient Pure Exploration in Adaptive Round model NeurIPS 2019 Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits AISTATS 2019