Title: Convergence analysis of controlled particle systems arising in deep learning
Abstract: This paper deals with a class of neural SDEs and studies the limiting behavior of the associated sampled optimal control problems as the sample size grows to infinity. The neural SDEs with N samples can be linked to the N-particle systems with centralized control. We analyze the Hamilton–Jacobi–Bellman equation corresponding to the N-particle system and establish regularity results which are uniform in N. The uniform regularity estimates are obtained by the stochastic maximum principle and the analysis of a backward stochastic Riccati equation. Using these uniform regularity results, we show the convergence of the minima of objective functionals and optimal parameters of the neural SDEs as the sample size N tends to infinity. The limiting objects can be identified with suitable functions defined on the Wasserstein space of Borel probability measures. Furthermore, quantitative algebraic convergence rates are also obtained.
Bio: Chao Zhou is a Quantitative Research Director at Qube Research & Technologies (QRT). Before joining QRT, he was an Associate Professor in the Department of Mathematics and Risk Management Institute at the National University of Singapore. He earned his PhD in Applied Mathematics from CMAP, Ecole Polytechnique, in France.