We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

The regularity of functions from reproducing kernel Hilbert spaces (RKHSs) is studied in the
setting of learning theory. We provide a reproducing property for partial derivatives up to …

We proposed the use of coordinate kernel polynomials in kernel regression. This new
approach, called coordinate kernel polynomial regression, can simultaneously identify …

We study the power of query access for the fundamental task of agnostic learning under the
Gaussian distribution. In the agnostic model, no assumptions are made on the labels of the …

In this paper, we consider unregularized online learning algorithms in a Reproducing Kernel
Hilbert Space (RKHS). Firstly, we derive explicit convergence rates of the unregularized …

Pairwise learning has recently received increasing attention since it subsumes many
important machine learning tasks (eg AUC maximization and metric learning) into a unifying …

K-nearest neighbors (KNN) is one of the earliest and most established algorithms in
machine learning. For regression tasks, KNN averages the targets within a neighborhood …

Pairwise learning usually refers to a learning task that involves a loss function depending on
pairs of examples, among which the most notable ones are bipartite ranking, metric learning …

Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a
normal cell to a malignancy and ultimately to a metastatic state. Certain genetic …

The gradient of a function defined on a manifold is perhaps one of the most important
differential objects in data analysis. Most often in practice, the input function is available only …