Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Recorded 01 March 2023. Pascal Van Hentenryck of the Georgia Institute of Technology presents "Fusing Machine Learning and Optimization" at IPAM's Artificial Intelligence and Discrete Optimization Workshop.
Abstract: The fusion of machine learning and optimization has the potential to achieve breakthroughs in decision making that the two technologies cannot accomplish independently. This talk reviews a number of research avenues in this direction, including the concept of optimization proxies and end-to-end learning. Principled combinations of machine learning and optimization are illustrated on case studies in energy systems, mobility, and supply chains. Preliminary results show how this fusion makes it possible to perform real-time risk assessment in energy systems, find near-optimal solutions quickly in supply chains, and implement model-predictive control for large-scale mobility systems.

[CristianoKlein]

Optimization is more pragmatic than ML. Can be more easily explainable than ML algorithms. ML could be a “black box” and harder to explaining they behaviors. It could be a challenging to transform the output from one to the input to other.