regret bound

The Importance of Pessimism in Fixed-Dataset Policy Optimization ICLR 2021 The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains NeurIPS 2021 Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation AAAI 2021 The Pareto Frontier of model selection for general Contextual Bandits NeurIPS 2021 The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks ICML 2021 Thompson Sampling Algorithms for Cascading Bandits JMLR 2021 Thompson Sampling for Bandits with Clustered Arms IJCAI 2021 Tight Regret Bounds for Infinite-armed Linear Contextual Bandits AISTATS 2021 Top-k eXtreme Contextual Bandits with Arm Hierarchy ICML 2021 Towards a Dimension-Free Understanding of Adaptive Linear Control COLT 2021 Towards Best-of-All-Worlds Online Learning with Feedback Graphs NeurIPS 2021 Towards Reducing Biases in Combining Multiple Experts Online IJCAI 2021 Towards tractable optimism in model-based reinforcement learning UAI 2021 Training a Single Bandit Arm AISTATS 2021 Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits JMLR 2021 UCB-based Algorithms for Multinomial Logistic Regression Bandits NeurIPS 2021 UCB Momentum Q-learning: Correcting the bias without forgetting ICML 2021 Understanding Bandits with Graph Feedback NeurIPS 2021 Understanding the Effect of Stochasticity in Policy Optimization NeurIPS 2021 Variational Bayesian Reinforcement Learning with Regret Bounds NeurIPS 2021 Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging ICML 2021 A Bandit Learning Algorithm and Applications to Auction Design NeurIPS 2020 Achieving Fairness in the Stochastic Multi-Armed Bandit Problem AAAI 2020 A Closer Look at Small-loss Bounds for Bandits with Graph Feedback COLT 2020 Active Learning of Conditional Mean Embeddings via Bayesian Optimisation UAI 2020