On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914158112407552 |
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| author | Ebrahimpour-Boroojeny, Ali Wang, Yian Sundaram, Hari |
| author_facet | Ebrahimpour-Boroojeny, Ali Wang, Yian Sundaram, Hari |
| contents | In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause privacy leakage. We further propose a simple yet effective solution to mitigate this issue. We introduce a membership-inference attack via nearest neighbors (MIA-NN) that uses the probabilities the model assigns to neighboring classes to detect unlearned samples. Our experiments show that existing unlearning methods are vulnerable to MIA-NN across multiple datasets. We then propose a new fine-tuning objective that mitigates this privacy leakage by approximating, for forget-class inputs, the distribution over the remaining classes that a retrained-from-scratch model would produce. To construct this approximation, we estimate inter-class similarity and tilt the target model's distribution accordingly. The resulting Tilted ReWeighting (TRW) distribution serves as the desired distribution during fine-tuning. We also show that across multiple benchmarks, TRW matches or surpasses existing unlearning methods on prior unlearning metrics. More specifically, on CIFAR-10, it reduces the gap with retrained models by 19% and 46% for U-LiRA and MIA-NN scores, accordingly, compared to the SOTA method for each category. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_20893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Ebrahimpour-Boroojeny, Ali Wang, Yian Sundaram, Hari Machine Learning Artificial Intelligence In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause privacy leakage. We further propose a simple yet effective solution to mitigate this issue. We introduce a membership-inference attack via nearest neighbors (MIA-NN) that uses the probabilities the model assigns to neighboring classes to detect unlearned samples. Our experiments show that existing unlearning methods are vulnerable to MIA-NN across multiple datasets. We then propose a new fine-tuning objective that mitigates this privacy leakage by approximating, for forget-class inputs, the distribution over the remaining classes that a retrained-from-scratch model would produce. To construct this approximation, we estimate inter-class similarity and tilt the target model's distribution accordingly. The resulting Tilted ReWeighting (TRW) distribution serves as the desired distribution during fine-tuning. We also show that across multiple benchmarks, TRW matches or surpasses existing unlearning methods on prior unlearning metrics. More specifically, on CIFAR-10, it reduces the gap with retrained models by 19% and 46% for U-LiRA and MIA-NN scores, accordingly, compared to the SOTA method for each category. |
| title | On the Necessity of Output Distribution Reweighting for Effective Class Unlearning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20893 |