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Model Selection for Off-Policy Evaluation via Pairwise Bellman Validation

Paper recorded by Signals 4 on 2026-10-26 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.LG · 机器学习 · first seen 2026-10-27

Abstract

Model selection and hyperparameter tuning pose a central challenge in off-policy evaluation (OPE) because errors of fitted Q-functions and occupancy-ratio estimates cannot be directly evaluated from held-out transitions. Bellman-residual and minimax validation methods often require auxiliary regressions or optimization over separate critic classes, adding computational and tuning costs. We introdu

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#171 most recent of 749 cs.LG papers we have recorded · ↑ newer: Improving Signals, Declining Encoder: A Known-Answer Test of Label-Fre · ↓ older: LUCID: Local Understanding of Covariate Importance with Conditional De
Cite this page: Model Selection for Off-Policy Evaluation via Pairwise Bellman Validation: the #171 most recent of 749 cs.LG papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/model-selection-for-off-policy-evaluation-via-pairwise-bellman-validation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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