stochastic algorithm

Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

-

Auditorium 0215 between B2 & B3

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

-

B2 L5 R5220

Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.

Slavomir Hanzely

Slavomir Hanzely is an MS/Ph.D. student in the Applied Mathematics and Computational Science Program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Slavomir earned his Bachelor's Degree in Informatics with a background in both Mathematics and Computer Science from the Faculty of Mathematics, Physics and Informatics University (FMPH UK), Slovakia in 2019. Research Interest He is interested in Machine Learning, Federated Learning, and Stochastic Algorithms. Education