The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.

No registration required.

The list of talks for the winter term 2019/20 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar

Abstract:
In this talk we present our recent results on a mathematical explanation for the empirical success of dynamic learning based on reservoir computing.
Motivated by the task of realized volatility forecasting, we study approximation and learning based on random recurrent neural networks and more general reservoir computing systems. For different types of echo state networks we obtain high-probability bounds on the approximation error in terms of the network parameters. For a more general class of reservoir computing systems and weakly dependent (possibly non-i.i.d.) input data, we then also derive generalization error bounds based on a Rademacher-type complexity.

The talk is based on joint work with Lyudmila Grigoryeva and Juan-Pablo Ortega.

Institute for Statistics and Mathematics