Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning

no code implementations • 7 Jan 2020 • Roula Nassif, Stefan Vlaski, Cedric Richard, Jie Chen, Ali H. Sayed

Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias.

BIG-bench Machine Learning Inductive Bias +1

Decentralized Online Learning with Kernels

no code implementations • 11 Oct 2017 • Alec Koppel, Santiago Paternain, Cedric Richard, Alejandro Ribeiro

That is, we establish that with constant step-size selections agents' functions converge to a neighborhood of the globally optimal one while satisfying the consensus constraints as the penalty parameter is increased.

General Classification Multi-class Classification +2

A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces

no code implementations • 14 Oct 2014 • Masa-aki Takizawa, Masahiro Yukawa, Cedric Richard

This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method.