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New framework optimizes logging policies for off-policy evaluation accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for designing logging policies to improve the accuracy of off-policy evaluation (OPE). OPE is crucial for estimating the performance of new policies, like recommender systems, using data collected by existing ones. The study identifies a key tradeoff between reward coverage and variance, proposing optimal logging policies for various scenarios where target policies and reward distributions are known, unknown, or partially known. The findings offer practical guidance for firms selecting recommendation systems and emphasize the importance of treatment selection in data gathering for OPE. AI

IMPACT Provides theoretical underpinnings for improving the evaluation of AI systems, particularly in recommendation and experimentation.

RANK_REASON Academic paper detailing a new framework and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework optimizes logging policies for off-policy evaluation accuracy

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Academic paper detailing a new framework and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Foster Provost ·

    Logging Policy Design for Off-Policy Evaluation

    Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging policy. It enables high-stakes experimentation without live deployment, yet in practice accuracy depends heavily on the logging po…

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