arXiv:2606. 20022v1 Announce Type: cross Abstract: This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise.
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The paper explores the use of set membership estimation (SME) for uncertainty quantification in linear contextual bandits, contrasting it with the more common confidence‑interval approach. An SME‑based optimism‑in‑the‑face‑of‑uncertainty (OFU) algorithm is proposed, and a sublinear regret bound is proven under suitable assumptions. Simulations show that SME‑OFU performs favorably compared to existing methods.
arXiv:2610.28136v1 Announce Type: cross Abstract: Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal. To obtain near-optimal r...
arXiv:2607. 23679v1 Announce Type: new Abstract: Recent years have witnessed increasing interests in tackling heteroscedastic noise in bandits and reinforcement learning.
arXiv:2606. 08028v1 Announce Type: new Abstract: We study high-probability regret bounds for online convex optimization (OCO) with strongly convex losses and establish three results that resolve open questions at the intersection of noise adaptivity, feedback structure, and constraint satisfaction.
arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.
arXiv:2608. 16492v1 Announce Type: cross Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization.