18 citations · 44 across the 8 of their papers we have counts for
8 papers
A Statistical Learning Assessment of Huber Regression
Yunlong Feng, Qiang Wu
As one of the triumphs and milestones of robust statistics, Huber regression plays an important role in robust inference and estimation. It has also been finding a great variety of…
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…
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…
Half-Quadratic Alternating Direction Method of Multipliers for Robust Orthogonal Tensor Approximation
Yuning Yang, Yunlong Feng
Higher-order tensor canonical polyadic decomposition (CPD) with one or more of the latent factor matrices being columnwisely orthonormal has been well studied in recent years. Howe…
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…
A Statistical Learning Approach to Modal Regression
Yunlong Feng, Jun Fan, Johan A. K. Suykens
This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the m…
Kernel Density Estimation for Dynamical Systems
Hanyuan Hang, Ingo Steinwart, Yunlong Feng +1
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from…
Learning theory estimates with observations from general stationary stochastic processes
Hanyuan Hang, Yunlong Feng, Ingo Steinwart +1
This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stat…