118 citations · 184 across the 10 of their papers we have counts for
14 papers
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…
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation
Marianne Defresne, Jayanta Mandi, Tias Guns
Real-life combinatorial optimization problems often involve several conflicting objectives, such as price, product quality and sustainability. A computationally-efficient way to ta…
Decision-Focused Learning to Predict Action Costs for Planning
Jayanta Mandi, Marco Foschini, Daniel Holler +3
In many automated planning applications, action costs can be hard to specify. An example is the time needed to travel through a certain road segment, which depends on many factors,…
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…