regret bound

Replicable Bandits ICLR 2023 Revisiting Weighted Strategy for Non-stationary Parametric Bandits AISTATS 2023 Reward Imputation with Sketching for Contextual Batched Bandits NeurIPS 2023 Riemannian Projection-free Online Learning NeurIPS 2023 Risk-aware linear bandits with convex loss AISTATS 2023 Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation ICLR 2023 Robust and private stochastic linear bandits ICML 2023 Robust Budget Pacing with a Single Sample ICML 2023 Robust Knowledge Transfer in Tiered Reinforcement Learning NeurIPS 2023 Robust Lipschitz Bandits to Adversarial Corruptions NeurIPS 2023 Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model ICML 2023 Sequential Counterfactual Risk Minimization ICML 2023 Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes COLT 2023 Short-lived High-volume Bandits ICML 2023 Smoothed Online Combinatorial Optimization Using Imperfect Predictions AAAI 2023 Smoothed Online Learning for Prediction in Piecewise Affine Systems NeurIPS 2023 Smooth Non-stationary Bandits ICML 2023 Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits AISTATS 2023 Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds NeurIPS 2023 Stochastic Contextual Bandits with Long Horizon Rewards AAAI 2023 Stochastic Graphical Bandits with Heavy-Tailed Rewards UAI 2023 Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk NeurIPS 2023 Stochastic No-regret Learning for General Games with Variance Reduction ICLR 2023 Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds NeurIPS 2023 The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning NeurIPS 2023