Decision-Focused Learning (and How to Do It Quickly)
Machine learning models are usually trained to be as accurate as possible: the more accurate the predictions, the better the model. But in practice, accuracy isn’t always what we actually care about. After all, predictions are only useful if they help us make good decisions. Traffic forecasts help us choose better routes, and stock price predictions guide investment decisions. This is where decision-focused learning comes in. It is a growing field in machine learning that aims to train models specifically to make predictions that lead to good decisions. However, this comes with significant challenges, including non-differentiable loss functions and computationally expensive training procedures. In this talk, I will introduce the main ideas behind decision-focused learning, highlight why it’s such a promising direction, and show how our recent work makes it much faster for one particularly important family of problems: linear optimization.
You can find the slides Senne used for his talk here.
Speaker
Senne Berden is a PhD student at the KU Leuven.
Time and Place
Wednesday 18/02/2026 at 13:45pm in M.G.006
Registration
Participation is free, but registration is compulsory: please fill in this form so that we can ensure there’s sandwiches for everyone