The paper introduces the first algorithms for replicable dueling bandits, ensuring that two runs with shared internal randomness and independent comparison outcomes select the same ordered pair in every round with high probability. Under relaxed stochastic transitivity, the authors achieve expected regret ×O(γ^8 K^4 H_p/ρ^2), while under strong stochastic transitivity they improve this to ×O(K^2 H_p/ρ^2). For linear dueling bandits, a regret bound of ×O(d^3√T/(c_μρ)) is presented, and experiments confirm both replicability and sublinear regret.
arXiv:2604. 20024v2 Announce Type: replace Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration.
arXiv:2402. 07391v3 Announce Type: replace-cross Abstract: We consider a replicable stochastic multi-armed bandit algorithm that ensures, with high probability, that the algorithm's sequence of actions is not affected by the randomness inherent in the dataset.
arXiv:2606. 01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret.
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
arXiv:2607. 23679v1 Announce Type: new Abstract: Recent years have witnessed increasing interests in tackling heteroscedastic noise in bandits and reinforcement learning.
arXiv:2608. 17841v1 Announce Type: cross Abstract: Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs.
Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs. We study the trade-off between worst-case regret $\mathcal{R}_{K,T}$ and instability $\mathcal S_{K,T}$, defined as the largest standard deviation of a terminal pull count, for $K$ arms and $T$ rounds.
arXiv:2610.07737v1 Announce Type: cross Abstract: We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rew...
arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...
The paper introduces Persistent Candidate LinUCB (PC‑LinUCB), a variant of the LinUCB algorithm for linear bandits that reduces per‑round score evaluations from ≥×K to ≥×(k+q) by maintaining a small set of active actions and sampling challengers from the remaining actions. It provides high‑probability regret bounds that include a standard learning term and an additive discovery cost of order ≥×((K-k)×log(K-k))/q, which does not grow with the horizon. Experiments demonstrate that PC‑LinUCB accelerates LinUCB while keeping regret comparable, and the persistent candidate approach can be applied beyond LinUCB.
arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.
arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.