18 citations · 44 across the 8 of their papers we have counts for
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cs.LG2020
A Framework of Learning Through Empirical Gain Maximization
Yunlong Feng, Qiang Wu
We develop in this paper a framework of empirical gain maximization (EGM) to address the robust regression problem where heavy-tailed noise or outliers may present in the response…
cs.LG2020
New Insights into Learning with Correntropy Based Regression
Yunlong Feng
Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively explored and studied. Its application t…
cs.LG2018★ 18 cited
Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise
Yunlong Feng, Yiming Ying
In recent years, correntropy and its applications in machine learning have been drawing continuous attention owing to its merits in dealing with non-Gaussian noise and outliers. Ho…