118 citations · 249 across the 32 of their papers we have counts for
8 papers · 1 filter
Learning to Learn in Interactive Constraint Acquisition
Dimos Tsouros, Senne Berden, Tias Guns
Constraint Programming (CP) has been successfully used to model and solve complex combinatorial problems. However, modeling is often not trivial and requires expertise, which is a…
Leveraging Contextual Information for Robustness in Vehicle Routing Problems
Ali İrfan Mahmutoğulları, Tias Guns
We investigate the benefit of using contextual information in data-driven demand predictions to solve the robust capacitated vehicle routing problem with time windows. Instead of e…
Holy Grail 2.0: From Natural Language to Constraint Models
Dimos Tsouros, Hélène Verhaeghe, Serdar Kadıoğlu +1
Twenty-seven years ago, E. Freuder highlighted that "Constraint programming represents one of the closest approaches computer science has yet made to the Holy Grail of programming:…
Guided Bottom-Up Interactive Constraint Acquisition
Dimos Tsouros, Senne Berden, Tias Guns
Constraint Acquisition (CA) systems can be used to assist in the modeling of constraint satisfaction problems. In (inter)active CA, the system is given a set of candidate constrain…
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi, James Kotary, Senne Berden +4
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an e…
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi +4
Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.g., demand or travel time…