Dagstuhl Seminar 24441
Machine Learning Augmented Algorithms for Combinatorial Optimization Problems
( Oct 27 – Oct 31, 2024 )
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Organizers
- Deepak Ajwani (University College Dublin, IE)
- Bistra Dilkina (USC - Los Angeles, US)
- Tias Guns (KU Leuven, BE)
- Ulrich Carsten Meyer (Goethe University - Frankfurt am Main, DE)
Contact
- Michael Gerke (for scientific matters)
- Susanne Bach-Bernhard (for administrative matters)
Dagstuhl Reports
As part of the mandatory documentation, participants are asked to submit their talk abstracts, working group results, etc. for publication in our series Dagstuhl Reports via the Dagstuhl Reports Submission System.
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- UCD CS academics invited to organise prestigious Dagstuhl seminars - University College Dublin School of Computer Science News, March 28, 2024
Combinatorial optimisation problems arise naturally in a multitude of crucial applications, ranging from business analytics, engineering, supply-chain optimisation, transportation, bioinformatics etc. In recent years, motivated by the success of machine learning in diverse fields, researchers have explored if learning techniques can be used to efficiently solve combinatorial optimisation problems. This is challenging because these problems have highly correlated decision variables and the correlations are long-range with very little spatial or temporal coherence. As a result, the end-to-end learning systems that take the problem instance as an input and produce the optimal solution as an output often do not generalise well to instances of larger sizes and from a different input distribution. Experts in this area have advocated for using machine learning in combination with current combinatorial optimisation algorithms to benefit from the theoretical guarantees and state-of-the-art algorithms already available.
The discussion in this Dagstuhl Seminar will focus on how best to combine the machine learning techniques with algorithmic insights and optimisation solvers to solve larger and harder instances of combinatorial optimisation problems arising from real-world applications. These discussions are expected to accelerate the pace of research in this area and build collaborations and synergies between the researchers working in the areas of algorithm design and engineering, combinatorial optimisation, and machine learning.
The seminar will provide a forum to discuss topics at the intersection of combinatorial optimisation, algorithm engineering, and machine learning:
- Learning-augmented algorithms and data structures
- Going beyond worst-case analysis using algorithms with predictions
- Machine learning augmented optimisation solvers
- Smart predict + optimise systems for applications where optimisation decisions are taken on data that is predicted using machine learning techniques
- Integrating algorithmic insights into machine learning techniques for solving optimisation problems.
Creative Commons BY 4.0
Deepak Ajwani, Bistra Dilkina, Tias Guns, and Ulrich Carsten Meyer
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- Deepak Ajwani (University College Dublin, IE) [dblp]
- Brandon Amos (Meta - New York, US) [dblp]
- Senne Berden (KU Leuven, BE)
- Quentin Cappart (Polytechnique Montréal, CA) [dblp]
- Eanna Curran (University College Dublin, IE)
- Marianne Defresne (KU Leuven, BE)
- Michelangelo Diligenti (University of Siena, IT)
- Bistra Dilkina (USC - Los Angeles, US) [dblp]
- Adam Elmachtoub (Columbia University - New York, US) [dblp]
- Aaron Ferber (Cornell University - Ithaca, US) [dblp]
- Alexandre Forel (Polytechnique Montréal, CA)
- Emma Frejinger (University of Montreal, CA) [dblp]
- Paul Grigas (University of California - Berkeley, US) [dblp]
- Ernestine Großmann (Universität Heidelberg, DE) [dblp]
- Tias Guns (KU Leuven, BE) [dblp]
- Mikhail Khodak (Princeton University, US)
- Sandra Kiefer (University of Oxford, GB) [dblp]
- Alex Lindermayr (Universität Bremen, DE)
- Michele Lombardi (University of Bologna, IT) [dblp]
- Nikolai Maas (KIT - Karlsruher Institut für Technologie, DE)
- Irfan Mahmutogullari (KU Leuven, BE) [dblp]
- Fredrik Manne (University of Bergen, NO) [dblp]
- Johannes Meintrup (THM - Gießen, DE) [dblp]
- Ulrich Carsten Meyer (Goethe University - Frankfurt am Main, DE) [dblp]
- Sofia Michel (NAVER Labs Europe - Meylan, FR) [dblp]
- Nysret Musliu (TU Wien, AT) [dblp]
- Mathias Niepert (Universität Stuttgart, DE) [dblp]
- Siegfried Nijssen (UC Louvain, BE & KU Leuven, BE) [dblp]
- Ryan O Connor (University College Dublin, IE)
- Manuel Penschuck (Goethe University - Frankfurt am Main, DE) [dblp]
- Adam Polak (Bocconi University - Milan, IT) [dblp]
- María Dolores Romero Morales (Copenhagen Business School, DK) [dblp]
- Louis-Martin Rousseau (Polytechnique Montréal, CA) [dblp]
- Jens Schlöter (CWI - Amsterdam, NL)
- Christian Schulz (Universität Heidelberg, DE) [dblp]
- Darren Strash (Hamilton College - Clinton, US) [dblp]
- Edward Tansley (University of Oxford, GB)
- Sylvie Thiébaux (University of Toulouse, FR & Australian National University, Canberra, AU) [dblp]
- Ali Vakilian (TTIC - Chicago, US) [dblp]
- Jacobus G. M. van der Linden (TU Delft, NL)
- Maurice Wenig (Friedrich-Schiller-Universität Jena, DE)
- Neil Yorke-Smith (TU Delft, NL) [dblp]
Classification
- Data Structures and Algorithms
- Discrete Mathematics
- Machine Learning
Keywords
- Combinatorial Optimisation
- Algorithm Engineering
- Machine Learning
- Constraint Solvers
