Year: 2023
Type: preprint
Abstract: We consider a deep neural network estimator based on empirical risk minimization with l_1-regularization. We derive a general bound for its excess risk in regression and classification (including multiclass), and prove that it is adaptively nearly-minimax (up to log-factors) simultaneously across the entire range of various function classes.
Source: arXiv (Cornell University)
Author: Abramovich, Felix
Institution: Tel Aviv University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Israel Science Foundation
OpenAlex ID: W4388748165