Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

We consider learning representations (features) in the setting in which we have access to
multiple unlabeled views of the data for representation learning while only one view is …

The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

The KeOps library provides a fast and memory-efficient GPU support for tensors whose
entries are given by a mathematical formula, such as kernel and distance matrices. KeOps …

Consider the classical supervised learning problem: we are given data (yi, xi), i≤ n, with yia
response and xi∈ X a covariates vector, and try to learn a model f ˆ: X→ R to predict future …

We study the generalization properties of ridge regression with random features in the
statistical learning framework. We show for the first time that $ O (1/\sqrt {n}) $ learning …

We study Nyström type subsampling approaches to large scale kernel methods, and prove
learning bounds in the statistical learning setting, where random sampling and high …

Approximation of non-linear kernels using random feature mapping has been successfully
employed in large-scale data analysis applications, accelerating the training of kernel …