Learning to optimize nonconvex problems: provably and efficiently
From 01-01-2020 to 31-12-2023
Description
Today's information society is centered on the collection of large amounts of data, from which machine learning (ML) aims at extracting information. The key step in any ML technique is "training", where an optimization problem is solved to tune the parameters of the ML model. Such optimization problems are becoming extremely challenging due to their large dimension and presence of highly nonconvex as well as nonsmooth terms. Optimization algorithms for solving learning problems addressing such problems usually involve a large number of hyperparameters that need to be hand-tuned after time-consuming experimentation and are prone to ill-conditioning and to getting stuck to local minima. As the size of datasets keeps growing rapidly and ML models become more complex, these issues are becoming more pronounced jeopardizing the applicability of ML techniques to an even wider area of applications. To address this challenge, in this project we aim at learning-based techniques for devising ad-hoc tuning-free optimization algorithms. A novel universal framework will be developed, which will serve as a solid theoretical ground for the development of new learning paradigms to train optimization methods subject to certificates of (speed of) convergence and quality of output solution.
Team
- Andreas Themelis, Co-promoter
- Panagiotis Patrinos, Promoter
Financing
Funding: FWO - Research Foundation - Flanders
Program/Grant Type: FWO Project - FWO Research Project
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.