This semester programme is planned for the second half of 2025, from September to November.
Summary
This semester programme aims to combine optimization and machine learning, in order to develop data-centric methods attaining both performance guarantees and explainability. This includes the design of algorithms incorporating machine-learned predictions of uncertain quality, methods using machine-learned models within algorithms, using machine-learning to model practical problems and using optimization techniques to analyze and improve machine learning algorithms. The programme consists of a bootcamp, a PhD School, two satellite workshops on different aspects of the topic and a seminar++ series. The organizational team comprises diverse international experts in optimization and machine learning, featuring both junior and senior researchers.
Introduction
Traditional optimization algorithms are based on abstract models that attempt to capture the essential properties of a practical problem. Such algorithms typically provide strong guarantees by assessing their performance on worst-case instances under this model, but which can be overly pessimistic with respect to their actual performance on practical instances. On the other hand, machine learning methods are trained and analyzed on practical data but generally offer no guarantee and poor explainability.
The scientific challenge addressed in this Research Semester Programme “Learning-Enhanced Optimization” is to combine optimization and machine learning to develop data-centric methods attaining both performance guarantees and explainability. This includes the design of algorithms incorporating machine-learned predictions of uncertain quality, methods using machine-learned models within an optimization algorithm, using machine-learning to model practical problems and using optimization techniques to analyze and improve algorithms used in machine learning.
This Research Semester Programme is organized by:
Karen Aardal (Delft), Antonios Antoniadis (Twente), Ilker Birbil (Amsterdam), Daniel Dadush (CWI), Dick den Hertog (Amsterdam), Ruben Hoeksma (Twente), Leo van Iersel (Delft), Etienne de Klerk (Tilburg), Monique Laurent (CWI), Guido Schaefer (CWI), Leen Stougie (CWI), Marc Uetz (Twente), Bert Zwart (CWI).
Programme
An overview of the events and their dates, is given below:
- Bootcamp: Interplay Between Machine Learning and Optimization
Duration: 1 day, early September 2025, date: Sep. 1, 2025
Goal: kick-off event highlighting the different aspects of the semester programme - International PhD School: Machine Learning and Optimization, 2-5 September, 2025
The PhD School explores recent theoretical developments at the intersection of machine learning and optimization, with a strong focus on theoretical and algorithmic foundations of how to exploit ML techniques in the design of algorithms. The school is aimed at PhD students and early-career researchers and combines talks with hands-on group work and collaborative discussions.
See here for the event page & tentative programme
- Satellite Workshop 1: Learning Augmented Algorithms, 7-10 October, 2025
This four-day workshop will bring together leading researchers of the field to discuss recent advancements, explore key challenges, and foster new collaborations.
The workshop will explore a wide range of topics within learning augmented algorithms, ranging from online algorithms to mechanism design and beyond. The programme will feature four keynote lectures, contributed and lightning talks, and provide ample time for open problem sessions and research discussions.
See here for the event page & tentative programme
- Satellite Workshop 2: Learned Methods for Operations Research, 3-6 November, 2025
This four-day workshop will bring together leading researchers working on the interface of optimization and machine learning to discuss recent advancements, explore key challenges, and foster new collaborations.
The workshop will explore a wide range of topics related to optimization, machine learning and applications, with the main focus on the use of machine learning within algorithm design. It aims to strengthen and widen research on all types of optimization, combined with any form of machine learning, both on a fundamental level as well as on direct applications, with an emphasis on the responsible use of machine learning.
The programme will feature keynote lectures and contributed talks, and provide ample time for research discussions.
See here for the event page & tentative programme
- Seminar++ Series: Advances in Fusing Optimization and Learning
Duration: half-day events, biweekly, dates TBD
Goal: open but with strong focus on getting Dutch community together, contributed talks, lightning talks by students, open problem plus collaboration session
The events are open to both national and international researchers. Students are especially welcome. The respective calls will be sent out in due time.
