regret bound

Fighting Bandits with a New Kind of Smoothness NeurIPS 2015 First-order regret bounds for combinatorial semi-bandits COLT 2015 Following the Perturbed Leader for Online Structured Learning ICML 2015 Generalized Mixability via Entropic Duality COLT 2015 Global Multi-armed Bandits with Hölder Continuity AISTATS 2015 Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints COLT 2015 High Dimensional Bayesian Optimisation and Bandits via Additive Models ICML 2015 Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning ICML 2015 Label optimal regret bounds for online local learning COLT 2015 Learning from Data with Heterogeneous Noise using SGD AISTATS 2015 Learning to Search Better than Your Teacher ICML 2015 Linear Multi-Resource Allocation with Semi-Bandit Feedback NeurIPS 2015 Minimax Time Series Prediction NeurIPS 2015 Non-Asymptotic Analysis of a New Bandit Algorithm for Semi-Bounded Rewards JMLR 2015 Online Density Estimation of Bradley-Terry Models COLT 2015 On-Line Learning Algorithms for Path Experts with Non-Additive Losses COLT 2015 Online Learning for Adversaries with Memory: Price of Past Mistakes NeurIPS 2015 Online Learning with Adversarial Delays NeurIPS 2015 Online Learning with Feedback Graphs: Beyond Bandits COLT 2015 Online Learning with Gaussian Payoffs and Side Observations NeurIPS 2015 Online Optimization : Competing with Dynamic Comparators AISTATS 2015 On the Complexity of Bandit Linear Optimization COLT 2015 Open Problem: Online Sabotaged Shortest Path COLT 2015 Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays ICML 2015 Qualitative Multi-Armed Bandits: A Quantile-Based Approach ICML 2015