regret bound

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem NeurIPS 2018 A Unified Framework for Extensive-Form Game Abstraction with Bounds NeurIPS 2018 Balanced Policy Evaluation and Learning NeurIPS 2018 Bandit Learning with Positive Externalities NeurIPS 2018 Bandit Online Learning on Graphs via Adaptive Optimization IJCAI 2018 Bandit Regret Scaling with the Effective Loss Range ALT 2018 Bandits with Delayed, Aggregated Anonymous Feedback ICML 2018 Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback IJCAI 2018 Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits JMLR 2018 Black-Box Reductions for Parameter-free Online Learning in Banach Spaces COLT 2018 Causal Bandits with Propagating Inference ICML 2018 Combinatorial Semi-Bandits with Knapsacks AISTATS 2018 Community Exploration: From Offline Optimization to Online Learning NeurIPS 2018 Competing with Automata-based Expert Sequences AISTATS 2018 Constant Regret, Generalized Mixability, and Mirror Descent NeurIPS 2018 Contextual Bandits with Stochastic Experts AISTATS 2018 Contextual bandits with surrogate losses: Margin bounds and efficient algorithms NeurIPS 2018 Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward NeurIPS 2018 Contextual Pricing for Lipschitz Buyers NeurIPS 2018 Coordinated Exploration in Concurrent Reinforcement Learning ICML 2018 Corrupt Bandits for Preserving Local Privacy ALT 2018 Cost-aware Cascading Bandits IJCAI 2018 Decision making with limited feedback ALT 2018 Differentially Private Contextual Linear Bandits NeurIPS 2018 Distributed Multi-Player Bandits - a Game of Thrones Approach NeurIPS 2018