regret bound

Making the most of your day: online learning for optimal allocation of time NeurIPS 2021 Matrix games with bandit feedback UAI 2021 Mean Field Equilibrium in Multi-Armed Bandit Game with Continuous Reward IJCAI 2021 Meta-Thompson Sampling ICML 2021 Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers NeurIPS 2021 Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition COLT 2021 Mirror Descent and the Information Ratio COLT 2021 Misspecification in Prediction Problems and Robustness via Improper Learning AISTATS 2021 Misspecified Gaussian Process Bandit Optimization NeurIPS 2021 Mixability made efficient: Fast online multiclass logistic regression NeurIPS 2021 Model-based Reinforcement Learning for Continuous Control with Posterior Sampling ICML 2021 MOTS: Minimax Optimal Thompson Sampling ICML 2021 Multi-armed Bandit Requiring Monotone Arm Sequences NeurIPS 2021 Multi-Armed Bandits with Cost Subsidy AISTATS 2021 Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning ICML 2021 Multinomial Logit Contextual Bandits: Provable Optimality and Practicality AAAI 2021 Multiplayer Bandit Learning, from Competition to Cooperation COLT 2021 Multi-Receiver Online Bayesian Persuasion ICML 2021 Multi-task and meta-learning with sparse linear bandits UAI 2021 Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021 (Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces COLT 2021 Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs NeurIPS 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021 Near-Optimal MNL Bandits Under Risk Criteria AAAI 2021 Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions AAAI 2021