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Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling

Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.LG · 机器学习 · first seen 2026-10-07

Abstract

Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly robust (DR) estimation remains unbiased under common support but retains these high-variance action-level weights. A prior estimator, Off-policy evaluation with Conjunct Effect Model (OffCEM), replaces them with more stable cluster-level weights, a

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#371 most recent of 762 cs.LG papers we have recorded · ↑ newer: A Systematic Study of Small Language Models on Abstract Reasoning Task · ↓ older: Direct Intermediate Initialization for Tilted Diffusion Samplers
Cite this page: Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling: the #371 most recent of 762 cs.LG papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/variance-optimal-off-policy-evaluation-with-conjunct-effect-modeling.html
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