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Author

Hang Pan

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Book Open access Aug 2026

Causality-Based Conformal Imputation Correction with Non-Random Missing Labels

This work proposes a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction, and leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set.

Chun-Yuan Zheng, Xiang Li, Hang Pan et al. · 0 citations
Jul 2026

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

LBR is proposed, a lightweight and model-agnostic framework for mitigating length bias in LLM-based recommendation that substantially alleviates length bias while consistently improving recommendation accuracy and fairness, with negligible additional training and inference overhead.

Hongchen Li, Bohao Wang, Jing-Bang Chen et al. · 0 citations

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