Statistical methods & theory

Girolami, M., Febrianto, E., Yin, G., & Cirak, F. The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. arXiv:1905.06391.

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]

Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization. Zhang, Ying ; Akyildiz, Ömer Deniz ; Damoulas, Theo ; Sabanis, Sotirios. 2019. arXiv:1910.02008 [math.ST]

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case Chau, N. H.; Moulines, É.; Rásonyi, M.; Sabanis, S.; Zhang, Y. 2018. In Print, SIAM Journal on Mathematics of Data Science. arXiv:1905.13142 [math.ST]

Data Study Group team. (2019, February 5). Data Study Group Final Report: Amnesty International. Zenodo. http://doi.org/10.5281/zenodo.2557805

Di Caterina, C. and Kosmidis, I. (2019). Location-adjusted Wald statistic for scalar parameters. Computational Statistics and Data Analysis, 138, 126-142

Kosmidis, I., Kenne Pagui, E.C. & Sartori, N. (2019). Statistics and Computing. https://doi.org/10.1007/s11222-019-09860-6