118 citations · 124 across the 7 of their papers we have counts for
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Predictive Dual Smoothing for Column Generation
Senne Berden, Noah Schutte, Andrea Lodi +1
Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alternates between solving the…
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…
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 İrfan Mahmutoğulları, 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…
Solver-Free Decision-Focused Learning for Linear Optimization Problems
Senne Berden, Ali İrfan Mahmutoğulları, 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…
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…