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Author

Cedar Site Bai

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#machine learning Preprint Aug 2026

Spectral Saliency for Machine Unlearning

Inspired by Muon, this work adopts the spectral view for unlearning and proposes Spectral Saliency Unlearning (SSU), a thresholding approach that thresholds weak singular components and updates only those directions supported by a confident unlearning signal.

Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh et al. · 0 citations
Preprint Aug 2026

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

This work proposes a new approach that quantifies the effectiveness of each interaction by the reduction in the assistant's uncertainty, measured via entropy over recommendations, to fine-tune the LLM, enabling strategic interaction generation.

Cedar Site Bai, Zhen-Yu Liao, Duan Li et al. · 0 citations

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