Abstract\emph {Contrastive learning}(CL) pretrains models in a pairwise manner, where
given a data point, other data points are all regarded as dissimilar, including some that …

In this paper, we provide a precise characterization of generalization properties of high
dimensional kernel ridge regression across the under-and over-parameterized regimes …

In many areas, practitioners need to analyze large data sets that challenge conventional
single-machine computing. To scale up data analysis, distributed and parallel computing …

Distributed learning based on the divide and conquer approach is a powerful tool for big
data processing. We introduce a distributed kernel gradient descent algorithm for the …

Correntropy-based learning has achieved great success in practice during the last decades.
It is originated from information-theoretic learning and provides an alternative to classical …

To cope with the challenges of memory bottleneck and algorithmic scalability when massive
data sets are involved, we propose a distributed least squares procedure in the framework of …

We study a learning algorithm for distribution regression with regularized least squares. This
algorithm, which contains two stages of sampling, aims at regressing from distributions to …

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

As one of the efficient approaches to deal with big data, divide-and-conquer distributed
algorithms, such as the distributed kernel regression, bootstrap, structured perception …

Theoretical analysis of the divide-and-conquer based distributed learning with least square
loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within the …