arXiv AI
Few-for-Many Best-Arm Identification in Multi-Objective Bandits
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 15
When More Objectives Make Identification Easier: The Role of Preferences and Feedback
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.
arXiv Machine Learning
6d ago
Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence
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.
arXiv AI
Sep 1
Mixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
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...
arXiv Machine Learning
Jun 16
Best Arm Identification with Minimal Regret
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.
arXiv AI
Sep 24
Softmax gradient policy for variance minimization and risk-averse multi armed bandits
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.
arXiv Machine Learning
Jun 15
A Complexity Measure for Active Learning in Multi-group Mean Estimation
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.