regret bound

Locally-Adaptive Nonparametric Online Learning NeurIPS 2020 (Locally) Differentially Private Combinatorial Semi-Bandits ICML 2020 Locally Differentially Private (Contextual) Bandits Learning NeurIPS 2020 Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems NeurIPS 2020 Logarithmic Regret for Adversarial Online Control ICML 2020 Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently ICML 2020 Making Non-Stochastic Control (Almost) as Easy as Stochastic NeurIPS 2020 Model-Based Reinforcement Learning with Value-Targeted Regression L4DC 2020 Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes ICML 2020 Model Selection in Contextual Stochastic Bandit Problems NeurIPS 2020 Monte-Carlo Graph Search: the Value of Merging Similar States ACML 2020 Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds AAAI 2020 My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits ICML 2020 Naive Exploration is Optimal for Online LQR ICML 2020 Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs IJCAI 2020 Near-optimal Regret Bounds for Stochastic Shortest Path ICML 2020 Neural Contextual Bandits with UCB-based Exploration ICML 2020 New Potential-Based Bounds for Prediction with Expert Advice COLT 2020 Non-Stationary Delayed Bandits with Intermediate Observations ICML 2020 Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without COLT 2020 No-Regret Prediction in Marginally Stable Systems COLT 2020 Observe Before Play: Multi-Armed Bandit with Pre-Observations AAAI 2020 Old Dog Learns New Tricks: Randomized UCB for Bandit Problems AISTATS 2020 On Adaptivity in Information-Constrained Online Learning AAAI 2020 On Approximate Thompson Sampling with Langevin Algorithms ICML 2020