PhD student, KU Leuven
2 papers at NeurIPS 2025
We propose a highly efficient loss function that exploits the geometry of linear optimization to enable fast solver-free training without compromising decision quality.
We propose a constraint-aware decision-focused learning framework that enables parameter prediction in constraints, without making any assumptions about the underlying optimization problem.
