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Yiwen Tu

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TMLR Journal 2026 Journal Article

Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution

Yiwen Tu
Ziqi Liu
Jiaqi W. Ma
Weijing Tang

Measuring task relatedness and mitigating negative transfer remain a critical open challenge in Multitask Learning (MTL). This work extends data attribution---which quantifies the influence of individual training data points on model predictions---to MTL setting for measuring task relatedness. We propose the MultiTask Influence Function (MTIF), a method that adapts influence functions to MTL models with hard or soft parameter sharing. Compared to conventional task relatedness measurements, MTIF provides a fine-grained, instance-level relatedness measure beyond the entire-task level. This fine-grained relatedness measure enables a data selection strategy to effectively mitigate negative transfer in MTL. Through extensive experiments, we demonstrate that the proposed MTIF efficiently and accurately approximates the performance of models trained on data subsets. Moreover, the data selection strategy enabled by MTIF consistently improves model performance in MTL. Our work establishes a novel connection between data attribution and MTL, offering an efficient and fine-grained solution for measuring task relatedness and enhancing MTL models.

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NeurIPS Conference 2025 Conference Paper

A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation

Yiwen Tu
Pingbang Hu
Jiaqi Ma

Machine unlearning updates machine learning models to remove information from specific training data samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics that lack theoretical understanding and reliability. Specifically, by modeling the proposed evaluation process as a cryptographic game between unlearning algorithms and MIA adversaries, the naturally-induced evaluation metric measures the data removal efficacy of unlearning algorithms and enjoys provable guarantees that existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient approximation of the induced evaluation metric and demonstrate its effectiveness through both theoretical analysis and empirical experiments. Overall, this work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.

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