Search Results for author:
Found 4 papers, 0 papers with code
Decision-focused predictions via pessimistic bilevel optimization: a computational study
In this work, we contribute to recent efforts in producing \emph{decision-focused} predictions, i. e., to build predictive models that are constructed with the goal of minimizing a \emph{regret} measure on the decisions taken with them.
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
The use of Mixed-Integer Linear Programming (MILP) models to represent neural networks with Rectified Linear Unit (ReLU) activations has become increasingly widespread in the last decade.
When Deep Learning Meets Polyhedral Theory: A Survey
In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing.
Principled Deep Neural Network Training through Linear Programming
Our results provide a new perspective on training problems through the lens of polyhedral theory and reveal a strong structure arising from these problems.
