Risk bounds for reservoir computing

Item Type Conference or Workshop Item (Paper)
Abstract We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing discrete-time inputs under various hypotheses on their dependence structure. Non-asymptotic bounds are explicitly written down in terms of the multivariate Rademacher complexities of the reservoir systems and the weak dependence structure of the signals that are being handled. This allows, in particular, to determine the minimal number of observations needed in order to guarantee a prescribed estimation accuracy with high probability for a given reservoir family. At the same time, the asymptotic behavior of the devised bounds guarantees the consistency of the empirical risk minimization procedure for various hypothesis classes of reservoir functionals.
Authors Gonon, Lukas; Grigoryeva, Lyudmila & Ortega, Juan-Pablo
Language English
Subjects computer science
other research area
Date 19 September 2019
Event Title Austrian Mathematical Society (ÖMG) Conference 2019
Event Location Dornbirn
Event Dates 16.-20.9.2019
Depositing User Dr. Lukas Gonon
Date Deposited 07 Nov 2019 09:49
Last Modified 20 Jul 2022 17:40
URI: https://www.alexandria.unisg.ch/publications/258270


Full text not available from this repository.


Gonon, Lukas; Grigoryeva, Lyudmila & Ortega, Juan-Pablo: Risk bounds for reservoir computing. 2019. - Austrian Mathematical Society (ÖMG) Conference 2019. - Dornbirn.


Edit item Edit item