arXiv:2609. 17595v1 Announce Type: new Abstract: In the improving multi-armed bandits problem, each of $k$ arms has an unknown nondecreasing, discretely concave reward curve $f_i$, and pulling arm $i$ for the $t$-th time yields $f_i(t)$.
arXiv:2609.15170v1 Announce Type: new Abstract: We study stochastic linear contextual bandits with arbitrary action menus that may depend on the fixed parameter and the interaction history. We establ...
arXiv:2608. 15996v1 Announce Type: new Abstract: We study second-order path-length regret in adversarial $K$-armed bandits against oblivious loss sequences.
arXiv:2608. 15365v1 Announce Type: new Abstract: Regret minimization (RM) and best-arm identification (BAI) are two fundamental objectives in multi-armed bandits.
arXiv:2609. 15248v2 Announce Type: replace-cross Abstract: In bandits with probing, a learner selects at most $k$ of $n$ arms per round, earns the largest of their rewards, and competes with the best fixed single arm.
arXiv:2609. 38659v1 Announce Type: cross Abstract: We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.
The paper investigates multi‑armed bandits where several arms are optimal. It refines previous sub‑sampling algorithms to achieve a minimax regret of τO((K−A)/√(KA)·√T), improving on earlier bounds. A matching lower bound is provided, showing the rate is nearly optimal, and the authors demonstrate that knowing the number of optimal arms A (within constant factors) is essential for near‑optimal performance.
In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards through the generalized $p$-mean, interpolating between utilitarian welfare ($p=1$), Nash welfare ($p\to0$), and Rawlsian fairness ($p\to-\infty$).
arXiv:2607. 13402v1 Announce Type: cross Abstract: In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials.
arXiv:2603. 02155v2 Announce Type: replace Abstract: Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqrt{T}$-type regret in the unregularized setting.
arXiv:2309. 06349v2 Announce Type: replace-cross Abstract: Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems.
arXiv:2511.05620v2 Announce Type: replace Abstract: We study worst-case dynamic regret of specific multi-armed bandit algorithms on piecewise-stationary instances with at most one breakpoint. Our con...
arXiv:2606. 14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight.