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arXiv Machine Learning

On Cost-Aware Designs for Sequential Hypothesis Testing

The paper introduces Cost-Aware Sequential Hypothesis Testing (CASHT), where a decision-maker selects sensing actions with varying random costs to identify the true hypothesis under an average-error constraint while minimizing expected total cost. For fixed costs, the optimal expected total cost scales as Θ(log(1/δ)) and can be achieved by Multihypothesis Sequential Probability Ratio Test-based procedures. The authors extend the framework to random costs under ex-post and ex-ante revelation models, analyze when action cancellation reduces cost, and demonstrate through simulations that CA variants consistently lower total cost compared to classical methods.

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arXiv Machine Learning
Aug 28

Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions

The paper introduces a new approach to safety in contextual bandits with continuous actions, focusing on high‑probability constraints on the realized cost rather than expected cost. It presents the High‑Probability Constrained UCB algorithm, which balances reward exploration with conservative safety estimation, and provides theoretical regret guarantees for linear models and extensions to general function classes. Experiments demonstrate that this realized‑cost safety framework significantly reduces safety violations compared to expected‑cost constrained methods.

By Spyros Dragazis, Aldo Pacchiano
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arXiv Machine Learning
Jun 5

Multi-Armed Sequential Hypothesis Testing by Betting

arXiv:2603. 17925v2 Announce Type: replace-cross Abstract: We consider a variant of sequential testing by betting where, at each time step, the statistician is presented with multiple data sources (arms) and obtains data by choosing one of the arms.

By Ricardo J. Sandoval, Ian Waudby-Smith, Michael I. Jordan
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arXiv Machine Learning
Aug 28

Sequential Additivity in Distributionally Robust Ranking and Selection

The paper studies distributionally robust ranking and selection (DRR&S), where the goal is to identify the best alternative under input uncertainty by considering multiple plausible input distributions. It introduces the concept of sequential additivity, showing that efficient sampling should focus on a small, additive set of critical scenarios rather than a multiplicative number. The authors prove an algorithm‑independent lower bound on sampling, design an additive allocation (AA) procedure that meets this bound and achieves exponentially decreasing error probability, and extend the approach to a general additive allocation (GAA) framework that incorporates traditional R&S sampling rules.

By Zaile Li, Yuchen Wan, L. Jeff Hong
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arXiv Machine Learning
Jul 13

Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

arXiv:2506. 03062v2 Announce Type: replace Abstract: A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.

By Qining Zhang, Tanner Fiez, Yi Liu, Wenyang Liu
safety
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arXiv Machine Learning
5d ago

Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

The paper explores how data from fixed A/B tests can guide the deployment of adaptive experiments using contextual bandits. By combining off‑policy evaluation with a controlled warm‑start simulation, the authors rank pre‑specified adaptive and non‑adaptive policies using doubly robust estimators. Experiments on synthetic trials and real benchmarks show that adaptive, context‑aware policies outperform fixed allocations when heterogeneity exists, but offer little advantage otherwise.

By Jo\~ao Victor Ferreira Alves, Eduardo Rocha Laurentino, Gustavo de Oliveira Kanno, Thiago Costa Rizuti da Rocha
benchmarks
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Hugging Face Trending Papers
Aug 27

Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions

The paper introduces a new approach to safety in contextual bandits with continuous actions by enforcing high‑probability constraints on the realized cost rather than on its expectation. It proposes the High‑Probability Constrained UCB algorithm, which balances optimistic reward exploration with pessimistic safety estimation. The authors provide theoretical regret guarantees for linear models and extend the analysis to general function classes, demonstrating experimentally that realized‑cost constraints significantly reduce safety violations compared to expected‑cost baselines.

agentsreinforcement-learningroboticssafety
More like this →