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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.03043 |
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| _version_ | 1866913700987797504 |
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| author | Rizwan, Hammad Sarvmaili, Mahtab Sajjad, Hassan Wu, Ga |
| author_facet | Rizwan, Hammad Sarvmaili, Mahtab Sajjad, Hassan Wu, Ga |
| contents | Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overlooking the varying difficulty of unlearning individual training samples. As a result, the broader feasibility of machine unlearning remains under-explored. This paper studies the cruxes that make machine unlearning difficult through a thorough instance-level unlearning performance analysis over various unlearning algorithms and datasets. In particular, we summarize four factors that make unlearning a data point difficult, and we empirically show that these factors are independent of a specific unlearning algorithm but only relevant to the target model and its training data. Given these findings, we argue that machine unlearning research should pay attention to the instance-level difficulty of unlearning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03043 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instance-Level Difficulty: A Missing Perspective in Machine Unlearning Rizwan, Hammad Sarvmaili, Mahtab Sajjad, Hassan Wu, Ga Machine Learning Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overlooking the varying difficulty of unlearning individual training samples. As a result, the broader feasibility of machine unlearning remains under-explored. This paper studies the cruxes that make machine unlearning difficult through a thorough instance-level unlearning performance analysis over various unlearning algorithms and datasets. In particular, we summarize four factors that make unlearning a data point difficult, and we empirically show that these factors are independent of a specific unlearning algorithm but only relevant to the target model and its training data. Given these findings, we argue that machine unlearning research should pay attention to the instance-level difficulty of unlearning. |
| title | Instance-Level Difficulty: A Missing Perspective in Machine Unlearning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.03043 |