4 citations · 4 across the 2 of their papers we have counts for
6 papers
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…
Scalable Decision-Focused Learning through Cost-Sensitive Regression
Noah Schutte, Senne Berden, Tias Guns +2
Many real-world combinatorial problems involve uncertain parameters, which can be predicted given contextual features and historical data. These `predict-then-optimize' or `context…
Solver-Free Decision-Focused Learning for Linear Optimization Problems
Senne Berden, Ali Ä°rfan MahmutoÄulları, Dimos Tsouros +1
Mathematical optimization is a fundamental tool for decision-making in a wide range of applications. However, in many real-world scenarios, the parameters of the optimization probl…
Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
Jayanta Mandi, Marianne Defresne, Senne Berden +1
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages: the prediction of t…
Minimizing Surrogate Losses for Decision-Focused Learning using Differentiable Optimization
Jayanta Mandi, Ali Ä°rfan MahmutoÄulları, Senne Berden +1
Decision-focused learning (DFL) trains a machine learning (ML) model to predict parameters of an optimization problem, to directly minimize decision regret, i.e., maximize decision…
Generalizing Constraint Models in Constraint Acquisition
Dimos Tsouros, Senne Berden, Steven Prestwich +1
Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback:…