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cs.LG2022
Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Yexiong Lin, Yu Yao, Yuxuan Du +4
Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of…
cs.LG2019★ 11 cited
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +1
A wide range of fundamental machine learning tasks that are addressed by the maximum a posteriori estimation can be reduced to a general minimum conical hull problem. The best-know…