learning theory

Learnability of Non-I.I.D. ACML 2016 Learning Combinatorial Functions from Pairwise Comparisons COLT 2016 Learning Theory for Distribution Regression JMLR 2016 Nearly Optimal Classification for Semimetrics AISTATS 2016 On the Recursive Teaching Dimension of VC Classes NeurIPS 2016 PAC-Bayesian Bounds based on the Rényi Divergence AISTATS 2016 Refined Error Bounds for Several Learning Algorithms JMLR 2016 Sample Complexity of Automated Mechanism Design NeurIPS 2016 The Limits of Learning with Missing Data NeurIPS 2016 The Optimal Sample Complexity of PAC Learning JMLR 2016 Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning NeurIPS 2016 The Teaching Dimension of Linear Learners ICML 2016 Agnostic Insurability of Model Classes JMLR 2015 Fast Rates for Exp-concave Empirical Risk Minimization NeurIPS 2015 Learning From Small Samples: An Analysis of Simple Decision Heuristics NeurIPS 2015 Learning Theory and Algorithms for Forecasting Non-stationary Time Series NeurIPS 2015 On the Complexity of Learning with Kernels COLT 2015 Strongly Adaptive Online Learning ICML 2015 Testing Closeness With Unequal Sized Samples NeurIPS 2015 Two-stage sampled learning theory on distributions AISTATS 2015 An Analysis of Active Learning with Uniform Feature Noise AISTATS 2014 A New Perspective on Learning Linear Separators with Large L_qL_p Margins AISTATS 2014 Consistency of weighted majority votes NeurIPS 2014 Ensemble Methods for Structured Prediction ICML 2014 Exploiting easy data in online optimization NeurIPS 2014