147 citations · 384 across the 17 of their papers we have counts for
4 papers · 1 filter
Optimizing over trained GNNs via symmetry breaking
Shiqiang Zhang, Juan S. Campos, Christian Feldmann +5
Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larg…
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette, Gonzalo Muñoz, Thiago Serra +1
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 visio…
Model-based feature selection for neural networks: A mixed-integer programming approach
Shudian Zhao, Calvin Tsay, Jan Kronqvist
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach. While the me…
Distributional constrained reinforcement learning for supply chain optimization
Jaime Sabal Bermúdez, Antonio del Rio Chanona, Calvin Tsay
This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Co…