A non-parametric extension of control variates is presented. These leverage gradient
information on the sampling density to achieve substantial variance reduction. It is not …

We focus on the distribution regression problem: regressing to vector-valued outputs from
probability measures. Many important machine learning and statistical tasks fit into this …

In this paper, we provide a mathematical foundation for the least square regression learning
with indefinite kernel and coefficient regularization. Except for continuity and boundedness …

Kernel-based methods in Numerical Analysis have the advantage of yielding optimal
recovery processes in the “native” Hilbert space ℋ H in which they are reproducing …

Distributed machine learning systems have been receiving increasing attentions for their
efficiency to process large scale data. Many distributed frameworks have been proposed for …

In this paper, we investigate the generalization performance of a regularized ranking
algorithm in a reproducing kernel Hilbert space associated with least square ranking loss …

Positive definiteness, reproducing kernel Hilbert spaces, integral operators and Mercer's
theorem in its various formats are common topics in many branches of mathematics. In this …

Ranking is always an important task in machine learning and information retrieval, eg,
collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic …

This paper presents an auxiliary model based hierarchical least squares algorithm to
estimate the parameters of single-input multi-output system modelling by combining the …

Let X be an open subset of article amssymb empty R^d and ν the restriction of the usual
Lebesgue measure of article amssymb empty R^d to X. In this paper, we investigate …