regret bound

DCM Bandits: Learning to Rank with Multiple Clicks ICML 2016 Delay and Cooperation in Nonstochastic Bandits COLT 2016 Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016 Double Thompson Sampling for Dueling Bandits NeurIPS 2016 Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions NeurIPS 2016 Efficient Algorithms for Adversarial Contextual Learning ICML 2016 Efficient Second Order Online Learning by Sketching NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits NeurIPS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NeurIPS 2016 Generalization and Exploration via Randomized Value Functions ICML 2016 High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models AISTATS 2016 Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits JMLR 2016 Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits NeurIPS 2016 Instance-dependent Regret Bounds for Dueling Bandits COLT 2016 Learning in Games: Robustness of Fast Convergence NeurIPS 2016 Machine Learning in an Auction Environment JMLR 2016 MetaGrad: Multiple Learning Rates in Online Learning NeurIPS 2016 Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games NeurIPS 2016 Multi-armed Bandits: Competing with Optimal Sequences NeurIPS 2016 Multi-Player Bandits – a Musical Chairs Approach ICML 2016 Multiple-Play Bandits in the Position-Based Model NeurIPS 2016 Multi-scale exploration of convex functions and bandit convex optimization COLT 2016 No-Regret Algorithms for Heavy-Tailed Linear Bandits ICML 2016 No Regret Bound for Extreme Bandits AISTATS 2016 Online Convex Optimization with Unconstrained Domains and Losses NeurIPS 2016