regret bound

Mirror Descent Meets Fixed Share (and feels no regret) NeurIPS 2012 Mixability in Statistical Learning NeurIPS 2012 Multi-armed Bandit Problems with History AISTATS 2012 Near-Optimal Algorithms for Online Matrix Prediction COLT 2012 No-Regret Algorithms for Unconstrained Online Convex Optimization NeurIPS 2012 On Bayesian Upper Confidence Bounds for Bandit Problems AISTATS 2012 Online L1-Dictionary Learning with Application to Novel Document Detection NeurIPS 2012 Online Optimization with Gradual Variations COLT 2012 Online Regret Bounds for Undiscounted Continuous Reinforcement Learning NeurIPS 2012 Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits AISTATS 2012 On Multilabel Classification and Ranking with Partial Feedback NeurIPS 2012 Open Problem: Better Bounds for Online Logistic Regression COLT 2012 Optimal Distributed Online Prediction Using Mini-Batches JMLR 2012 Risk-Aversion in Multi-armed Bandits NeurIPS 2012 Selective Sampling and Active Learning from Single and Multiple Teachers JMLR 2012 Stochastic Bandit Based on Empirical Moments AISTATS 2012 The Best of Both Worlds: Stochastic and Adversarial Bandits COLT 2012 Towards Minimax Policies for Online Linear Optimization with Bandit Feedback COLT 2012 Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints JMLR 2012 Unified Algorithms for Online Learning and Competitive Analysis COLT 2012 Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity NeurIPS 2011 Adaptive Bandits: Towards the best history-dependent strategy AISTATS 2011 Adaptive Hedge NeurIPS 2011 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization JMLR 2011 A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences COLT 2011