Bryan Kian Hsiang Low
2 indexed papers
Recent (6 mo)
2With code
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226
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ML×2AI×2
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Research Timeline
2026
De-attribute to Forget for LLM Unlearning
The paper proposes DareU, a novel LLM unlearning framework that optimizes unlearning by zeroing out data attribution scores instead of maximizing prediction loss, achieving effective unlearning while maintaining model utility.
How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
The paper proposes a novel, theoretically-grounded algorithm (HAMU) that addresses the challenge of machine unlearning by guaranteeing specified improvements in forget quality while minimizing retain utility degradation.
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Papers
cs.LGcs.AIRecentJun 1, 2026
How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
The paper proposes a novel, theoretically-grounded algorithm (HAMU) that addresses the challenge of machine unlearning by guaranteeing specified improvements in forget quality while minimizing retain…
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cs.LGcs.AIRecentMay 29, 2026