aiqbench analysis
This research introduces C-ICPE, a novel framework combining theoretical guidance with learned models to solve pure exploration problems in continuous decision spaces. By meta-training sequential architectures over task priors, the system effectively handles scenarios like continuous-armed bandits and function minimization without requiring parameter updates at inference time. This advancement addresses critical limitations of existing frequentist methods that are often restricted to finite recommendation spaces or specific model structures.
In-Context Pure Exploration in Continuous Decision Spaces
arXiv:2602.17976v2 Announce Type: replace Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible. This problem has several motivating applications, including Best-Arm Identification (BAI) in bandits, where actions index hypotheses, and generalized search problems, where strategically chosen queries reveal partial information about a hidden label. In many modern settings, however, the hypothesis, or recommendation space, is continuous: for example, identifying a near optimal action in a continuous-armed bandit, localizing an $\epsilon$-ball contained in a target region, or estimating the minimizer of a function from noisy observations. Existing methods are predominantly frequentist and model-specific, while learned approaches have been limited to finite recommendation spaces. We introduce C-ICPE, a theory-guided learned model for Bayesian fixed-confidence pure exploration with continuous recommendations. C-ICPE meta-trains sequential architectures over a task prior to jointly learn exploration, stopping and recommendations strategies. At inference time, it actively gathers evidence on tasks and identifies an $\epsilon$-optimal recommendation without parameter updates.
Abstract:In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible. This problem has several motivating applications, including Best-Arm Identification (BAI) in bandits, where actions index hypotheses, and generalized search problems, where strategically chosen queries reveal partial information about a hidden label. In many modern settings, however, the hypothesis, or recommendation space, is continuous: for example, identifying a near optimal action in a continuous-armed bandit, localizing an $\epsilon$-ball contained in a target region, or estimating the minimizer of a function from noisy observations. Existing methods are predominantly frequentist and model-specific, while learned approaches have been limited to finite recommendation spaces. We introduce C-ICPE, a theory-guided learned model for Bayesian fixed-confidence pure exploration with continuous recommendations. C-ICPE meta-trains sequential architectures over a task prior to jointly learn exploration, stopping and recommendations strategies. At inference time, it actively gathers evidence on tasks and identifies an $\epsilon$-optimal recommendation without parameter updates.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.17976 [cs.LG]
(or arXiv:2602.17976v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.17976 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 265 words
