Reserve-Aware Contrast Certificates for Conservative Bandits with Uncertain Baselines
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Abstract (arXiv)
Conservative bandits must improve an incumbent policy without exhausting a prescribed performance budget. When the incumbent is uncertain, separately bounding candidate and baseline rewards can charge twice for shared estimation error. We develop Reserve-C4B around the baseline-relative contrast itself. A shared confidence set yields an exact expression for this avoidable penalty and a tighter admissibility test at every fixed history. A reserve ledger separates statistical evidence from permitted performance deficit; a prefix-refresh extension recertifies accumulated decisions under the current confidence set without discarding previously certified credit. For linear rewards, self-normalized confidence sets provide simultaneous validity over time and adaptively generated candidates, and the resulting policy satisfies a conditional-mean performance constraint with high probability. Reproducible experiments isolate certificate coupling, prefix refresh, and historical information, showing large reductions in baseline fallback while exposing the limitations of frozen certificates.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 30 Sep 2026.
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8 of 10 references.
References · 8
- 2016Distribution-Free Predictive Inference For Regressioncited by 668
- 2016Conservative Banditscited by 59
- 2016Conservative Contextual Linear Banditscited by 57
- 2016Safe Policy Improvement by Minimizing Robust Baseline Regretcited by 53
- 2020Improved Algorithms for Conservative Exploration in Banditscited by 16
- 2021A Reduction-Based Framework for Conservative Bandits and Reinforcement Learningcited by 9
- 2019Contextual Combinatorial Conservative Banditscited by 4
- 2024Conservative Contextual Bandits: Beyond Linear Representationscited by 4
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