In the past years, the generalized maximum correntropy criterion (GMCC) has been widely
used in adaptive filters to provide robust behavior under non-Gaussian/impulsive noise …

Convolutional neural networks (CNNs) have gained immense popularity in recent years,
finding their utility in diverse fields such as image recognition, natural language processing …

Pairwise learning refers to learning tasks where the loss function depends on a pair of
instances. It instantiates many important machine learning tasks such as bipartite ranking …

Within the statistical learning framework, this paper studies the regression model associated
with the correntropy induced losses. The correntropy, as a similarity measure, has been …

This paper studies the nonparametric modal regression problem systematically from a
statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of …

Minimum error entropy (MEE) is an information theoretic learning approach that minimizes
the information contained in the prediction error, which is measured by entropy. It has been …

In this paper, we develop the weighted error entropy-regularized tensor learning method for
multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal …

The minimum error entropy (MEE) is an important and highly effective optimization criterion
in information theoretic learning (ITL). For regression problems, MEE aims at minimizing the …

Pairwise learning is widely employed in ranking, similarity and metric learning, area under
the ROC curve (AUC) maximization, and many other learning tasks involving sample pairs …

This textbook provides an in-depth exploration of statistical learning with reproducing
kernels, an active area of research that can shed light on trends associated with deep neural …