conftrace_

regret bound

1926 papers

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Also known as

RB

Co-occurring keywords

online learning (1777) multi-armed bandit (1098) contextual bandit (381) stochastic optimization (1060) reinforcement learning (4352) online algorithm (445) upper confidence bound (197) markov decision process (790) thompson sampling (237) online convex optimization (165)

Papers

CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption ALT 2024
On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games NIPS 2024
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum AISTATS 2024
Sum-max Submodular Bandits AISTATS 2024
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness ICML 2023
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes ICML 2023
Smooth Non-stationary Bandits ICML 2023
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes ICML 2023
Benefits of monotonicity in safe exploration with Gaussian processes UAI 2023
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation AISTATS 2023
Bandits with costly reward observations UAI 2023
Doubly Fair Dynamic Pricing AISTATS 2023
Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits? UAI 2023
Bounding the optimal value function in compositional reinforcement learning UAI 2023
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo AISTATS 2023
Nonstochastic Contextual Combinatorial Bandits AISTATS 2023
Reinforcement Learning with History Dependent Dynamic Contexts ICML 2023
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards ICML 2023
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents ICML 2023
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits AISTATS 2023
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits AISTATS 2023
Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023
Heavy-tailed linear bandit with Huber regression UAI 2023
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning UAI 2023
Unconstrained Online Learning with Unbounded Losses ICML 2023