Monte Carlo methods

D. Thesingarajah and A. M. Johansen. The Node-wise Pseudo-marginal Method. Statistics and Computing, 32(43) 1-31: 2022.

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks. Lim, D-Y. & Sabanis, S. 2021. arXiv:2105.13937 [cs.LG]

N. J. Tye, J. T. Meech, B. A. Bilgin and P. Stanley-Marbell, "A System for Generating Non-Uniform Random Variates using Graphene Field-Effect Transistors," 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP), Manchester, United Kingdom, 2020, pp. 101-108, doi: 10.1109/ASAP49362.2020.00026.

J. T. Meech and P. Stanley-Marbell, "Efficient Programmable Random Variate Generation Accelerator from Sensor Noise," in IEEE Embedded Systems Letters, doi: 10.1109/LES.2020.3007005

A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating. Sabanis, Sotirios; Zhang, Ying. arXiv:2007.01672 [q-fin.PM]

Taming neural networks with TUSLA : Non-convex learning via adaptive stochastic gradient Langevin algorithms. Lovas, Attila; Lytras, Iosif ; Rásonyi, Miklós ; Sabanis, Sotirios. arXiv:2006.14514 [cs.LG]

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. Akyildiz, Ömer Deniz ; Sabanis, Sotirios. arXiv:2002.05465 [math.OC]

Yue Wu, Nick Polydorides, "A Multilevel Monte Carlo Estimator for Matrix Multiplication”, arxiv.org/abs/1904.00429