The paper investigates best‑arm identification (BAI) in multi‑objective bandits, showing that additional objectives can actually reduce the number of samples needed. For Gaussian bandits, a characteristic‑time lower bound is derived that applies uniformly across preference orders, and explicit comparisons under Pareto, linear, and lexicographic preferences reveal that a larger optimal set can be identified with fewer samples, that the benefit of vector feedback depends on the preference, and that multi‑objective BAI can outperform the easiest single‑objective case. The authors also introduce Order‑Aware Track‑and‑Stop (OATS), an algorithm that achieves the lower bound while guaranteeing correctness at any desired confidence level.
By Bo Xue, Ji Cheng, Yuanyu Wan, Shuang Qiu
The paper introduces Halve-Then-Greedy (HTG), an adaptive sampling strategy for fixed-budget experimentation that maximizes the probability of obtaining sufficient posterior evidence that a recommended design’s mean performance exceeds a target. HTG combines an initial balanced allocation, successive halving, and posterior-greedy sampling, and the authors prove instance-dependent, finite-budget exponential bounds on posterior-failure probability. They also establish that HTG matches an asymptotic sample-complexity lower bound up to a logarithmic factor and demonstrate, through analysis and simulations, that HTG outperforms uniform sampling and sequential halving in terms of sample efficiency.
By Yinan Li, Kwang-Sung Jun, Kapilan Balagopalan, Tuan Nguyen, Yao Zhao, Abbas Zaidi, Houssam Nassif
arXiv:2603.21716v2 Announce Type: replace-cross
Abstract: Efficient selection among multiple generative models is increasingly important in modern generative AI, where sampling from suboptimal models...
By Bahar Dibaei Nia, Farzan Farnia
arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.
By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin
The paper introduces a new algorithm for the Multi‑Armed Bandit problem that prioritizes selecting the arm with the lowest variance rather than the highest expected reward, using a softmax policy parameterization. It constructs an unbiased estimate of the minimal‑variance objective by drawing two independent samples from the chosen arm and proves convergence under natural conditions. Numerical experiments demonstrate the algorithm’s practical behavior and provide implementation guidance, while also addressing general risk‑aware trade‑offs between average reward and variance.
By Gabriel Turinici
arXiv:2606. 14690v1 Announce Type: new Abstract: We study a \emph{max-risk} objective for active learning in a multi-group mean estimation $d$-armed bandits: a learner adaptively allocates a budget of $T$ samples across $d$ groups to minimize the worst-case uncertainty index $\max_{k\in[d]}\sigma_k^2/n_k$, where $\sigma_k$ is the standard deviation of the distribution of arm $d$, and $n_k$ is the number of times arm $d$ is sampled.
By Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
arXiv:2608. 01545v1 Announce Type: cross Abstract: We study the problem of identifying the dominant arm in multi-armed bandits, where the objective is to find the action with the highest probability of exceeding the realized rewards of all other actions.
By Jonghyun Sim, Wonyoung Kim
arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.
By Deqi Zheng, Xiaoyang Xu, Yuhong Yang
arXiv:2606. 08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper.
By Vladimir Braverman, Chen Wang, Liudeng Wang, Samson Zhou
arXiv:2607. 14706v1 Announce Type: new Abstract: We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits.
By Xin Li, Zixin Zhong
The paper introduces a new variant of the multi‑armed bandit problem that incorporates dueling feedback—pairwise comparisons of model responses—and heterogeneous sampling costs to identify the best large language model (LLM) from a set with varying query costs. Assuming a Condorcet winner, the authors propose a Track‑and‑Stop style algorithm that guarantees asymptotically optimal cost as the error probability approaches zero. Extensive experiments on synthetic and real‑world data show that this cost‑aware approach consistently outperforms both classical cost‑unaware algorithms and other cost‑aware extensions.
By Sarvesh Gharat, Nikhil Karamchandani, Jayakrishnan Nair
arXiv:2506. 24007v5 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies for fixed-budget best-arm identification.
By Masahiro Kato