Publications of Eduardo D. Sontag jointly with L. Gurvits
Articles in journal or book chapters
  1. M. J. Donahue, L. Gurvits, C. Darken, and E.D. Sontag. Rates of convex approximation in non-Hilbert spaces. Constr. Approx., 13(2):187-220, 1997. [PDF] Keyword(s): neural networks, optimization, approximation theory.
    This paper deals with sparse approximations by means of convex combinations of elements from a predetermined "basis" subset S of a function space. Specifically, the focus is on the rate at which the lowest achievable error can be reduced as larger subsets of S are allowed when constructing an approximant. The new results extend those given for Hilbert spaces by Jones and Barron, including in particular a computationally attractive incremental approximation scheme. Bounds are derived for broad classes of Banach spaces. The techniques used borrow from results regarding moduli of smoothness in functional analysis as well as from the theory of stochastic processes on function spaces.

Conference articles
  1. C. Darken, M.J. Donahue, L. Gurvits, and E.D. Sontag. Rate of approximation results motivated by robust neural network learning. In COLT '93: Proceedings of the sixth annual conference on Computational learning theory, New York, NY, USA, pages 303-309, 1993. ACM Press. [doi:] Keyword(s): neural networks, optimization problems, approximation theory.



This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.

Last modified: Thu Nov 23 10:40:56 2017
Author: sontag.

This document was translated from BibTEX by bibtex2html