Mini workshop: Machine Learning meets Optimization

16 december 2021 09:00 - Locatie: HYBRIDE / BUILDING PULSE, HALL 9 | Zet in mijn agenda

Machine learning and Optimization are closely related. On the one hand, many machine learning tasks can be solved using optimizers, on the other hand combinatorial optimization problems (static, dynamic, stochastic) are increasingly solved effectively with the help of machine learning. The aim of the half-day workshop is to present talks and have open discussions by researchers from Machine Learning and Operations Research in order to identify how techniques from the two fields cross-fertilize.

Registration for both in person or online attendance required, at no cost via:

Programme (hybrid)

09:00             Patrick De Causmaecker (KU Leuven)
A New Class of Hard Problem Instances for the 0-1 Knapsack Problem and their position in Instance Space.
09:25 Laurens Bliek (TU Eindhoven)
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
09:50 Herke van Hoof (University of Amsterdam)
Learning New Heuristics for Combinatorial Problems
10:15 Coffee break
10:40 Hoong Chuin Lau (Singapore Management University)
Machine Learning meets Combinatorial Optimization: Real World Applications in Routing and Scheduling
11:05 Wendelin Böhmer (TU Delft)
Learning from Failure: Deep Reinforcement Learning for Optimization
11:30 Lightning pitches


  • Patrick De Causmaecker (KU Leuven, BE)
  • Neil Yorke-Smith (TU Delft, NL)
  • Yingqian Zhang (TU Eindhoven, NL)


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