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New research tackles cross-domain OPE/L for contextual bandits

By PulseAugur Editorial · [2 sources] ·

A new research paper introduces Cross-Domain Off-Policy Evaluation and Learning (OPE/L) for contextual bandits, addressing limitations in existing methods. The proposed approach allows for the evaluation and learning of new policies using historical data from both the target domain and other related domains. This novel formulation aims to enhance OPE/L performance in challenging scenarios such as limited data, deterministic logging policies, and the introduction of new actions, which are common in fields like personalized medicine and content recommendations. AI

IMPACT This research could improve the effectiveness of personalized systems by enabling better policy evaluation and learning in domains with limited or complex historical data.

RANK_REASON The item is an academic paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research tackles cross-domain OPE/L for contextual bandits

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The item is an academic paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ruiyuan Huang, Zengfeng Huang ·

    Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback

    arXiv:2502.04678v2 Announce Type: replace Abstract: Repeated first-price auctions are contextual decision problems with censored but reusable feedback: after submitting a bid, a learner can infer the outcomes of related bids and evaluate them under different private values. This …

  2. arXiv cs.LG TIER_1 English(EN) · Yuta Natsubori, Masataka Ushiku, Yuta Saito ·

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

    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. However, existing methods i…

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