research
∙
03/23/2023
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
We study a class of interacting particle systems for implementing a marg...
share
research
∙
01/19/2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity – the Strongly Convex Case
In this article we consider sampling from log concave distributions in H...
research
∙
10/24/2022
Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient
We introduce a new Langevin dynamics based algorithm, called e-THεO POUL...
research
∙
12/31/2021
A Strongly Monotonic Polygonal Euler Scheme
Rate of convergence results are presented for a new class of explicit Eu...
research
∙
10/21/2021
Statistical Finite Elements via Langevin Dynamics
The recent statistical finite element method (statFEM) provides a cohere...
research
∙
07/19/2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
We consider non-convex stochastic optimization problems where the object...
research
∙
05/28/2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
We present a new class of adaptive stochastic optimization algorithms, w...
research
∙
07/02/2020
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating
A new approach in stochastic optimization via the use of stochastic grad...
research
∙
06/25/2020
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
Artificial neural networks (ANNs) are typically highly nonlinear systems...
research
∙
02/13/2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
We provide a nonasymptotic analysis of the convergence of the stochastic...
research
∙
10/04/2019
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
Within the context of empirical risk minimization, see Raginsky, Rakhlin...
research
∙
05/30/2019
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution which is ...
research
∙
08/02/2018
