arXiv Machine Learning July 27, 2026 By Yuta Natsubori, Masataka Ushiku, Yuta Saito

Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits

Read the original on arXiv Machine Learning →

arXiv:2607. 22012v1 Announce Type: new Abstract: Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

reinforcement-learning

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Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation

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Jul 31

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agents diffusion safety

arXiv Machine Learning

Aug 18

Sequential Batch Learning in Finite-Action Linear Contextual Bandits

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reinforcement-learning

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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.

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benchmarks

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