1 citations · 1 across the 4 of their papers we have counts for
4 papers
Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
Luxu Liang, Ariel Neufeld, Ying Zhang
In this paper, we provide a non-asymptotic analysis of the convergence of the stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm to a target measure in Wasserstein-1 and…
Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
Ariel Neufeld, Ying Zhang
In this paper, we propose two new algorithms, namely, aHOLA and aHOLLA, to sample from high-dimensional target distributions with possibly super-linearly growing potentials. We est…
Non-concave stochastic optimal control in finite discrete time under model uncertainty
Ariel Neufeld, Julian Sester
In this article we present a general framework for non-concave robust stochastic control problems under model uncertainty in a discrete time finite horizon setting. Our framework a…
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems
Ariel Neufeld, Matthew Ng Cheng En, Ying Zhang
In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm tailored for solving a certain class of non-convex distributionally robust optimisation (DRO) prob…