regret bound

On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems NeurIPS 2019 On the optimality of the Hedge algorithm in the stochastic regime JMLR 2019 Open Problem: Risk of Ruin in Multiarmed Bandits COLT 2019 Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards ICML 2019 Optimal Exploitation of Clustering and History Information in Multi-armed Bandit IJCAI 2019 Optimistic Policy Optimization via Multiple Importance Sampling ICML 2019 Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback NeurIPS 2019 Parameter-Free Online Convex Optimization with Sub-Exponential Noise COLT 2019 Personalizing Many Decisions with High-Dimensional Covariates NeurIPS 2019 Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints NeurIPS 2019 Policy Certificates: Towards Accountable Reinforcement Learning ICML 2019 POLITEX: Regret Bounds for Policy Iteration using Expert Prediction ICML 2019 Polynomial Cost of Adaptation for X-Armed Bandits NeurIPS 2019 Prior-Free Dynamic Auctions with Low Regret Buyers NeurIPS 2019 Problem-dependent Regret Bounds for Online Learning with Feedback Graphs UAI 2019 Recovering Bandits NeurIPS 2019 Reducing Exploration of Dying Arms in Mortal Bandits UAI 2019 Regret Bounds for Learning State Representations in Reinforcement Learning NeurIPS 2019 Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems NeurIPS 2019 Riemannian Adaptive Optimization Methods ICLR 2019 Risk-Averse Stochastic Convex Bandit AISTATS 2019 Robust Frequent Directions with Application in Online Learning JMLR 2019 Rotting bandits are no harder than stochastic ones AISTATS 2019 Sample-Optimal Parametric Q-Learning Using Linearly Additive Features ICML 2019 Semi-Parametric Dynamic Contextual Pricing NeurIPS 2019