vc dimension

Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization ICML 2018 Model-Agnostic Private Learning NeurIPS 2018 Optimal Quantum Sample Complexity of Learning Algorithms JMLR 2018 PAC-learning in the presence of adversaries NeurIPS 2018 SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data ICLR 2018 Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NeurIPS 2018 Inapproximability of VC Dimension and Littlestone’s Dimension COLT 2017 Mixing Implies Lower Bounds for Space Bounded Learning COLT 2017 On the Complexity of Learning from Label Proportions IJCAI 2017 Risk Bounds for Transferring Representations With and Without Fine-Tuning ICML 2017 On the Recursive Teaching Dimension of VC Classes NeurIPS 2016 Refined Error Bounds for Several Learning Algorithms JMLR 2016 Sign rank versus VC dimension COLT 2016 An Almost Optimal PAC Algorithm COLT 2015 Minimax Analysis of Active Learning JMLR 2015 Open Problem: Recursive Teaching Dimension Versus VC Dimension COLT 2015 Sample Complexity Bounds on Differentially Private Learning via Communication Complexity COLT 2014 Sample Compression for Multi-label Concept Classes COLT 2014 The sample complexity of agnostic learning under deterministic labels COLT 2014 Honest Compressions and Their Application to Compression Schemes COLT 2013 Predictive PAC Learning and Process Decompositions NeurIPS 2013 A Geometric Approach to Sample Compression JMLR 2012 Dimensionality Dependent PAC-Bayes Margin Bound NeurIPS 2012 Distributed Learning, Communication Complexity and Privacy COLT 2012 Learning Multiple Tasks using Shared Hypotheses NeurIPS 2012