regret bound

The Gain from Ordering in Online Learning NeurIPS 2023 The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning ICML 2023 Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits ICML 2023 Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023 Thresholded linear bandits AISTATS 2023 Tighter PAC-Bayes Bounds Through Coin-Betting COLT 2023 Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient COLT 2023 Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo AISTATS 2023 Tracking Most Significant Shifts in Nonparametric Contextual Bandits NeurIPS 2023 Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts ICML 2023 Trading-off price for data quality to achieve fair online allocation NeurIPS 2023 TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation AISTATS 2023 U-Calibration: Forecasting for an Unknown Agent COLT 2023 Unconstrained Online Learning with Unbounded Losses ICML 2023 Understanding the Role of Feedback in Online Learning with Switching Costs ICML 2023 Uniformly Conservative Exploration in Reinforcement Learning AISTATS 2023 Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach NeurIPS 2023 Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory ACML 2023 VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback JMLR 2023 VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation COLT 2023 Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality ICML 2023 What can online reinforcement learning with function approximation benefit from general coverage conditions? ICML 2023 “Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization NeurIPS 2023 Zero-Regret Performative Prediction Under Inequality Constraints NeurIPS 2023 πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization ICLR 2022