Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

We develop, in this paper, a machine learning approach to optimize the real-time operation
of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve
sequential decision-making problems by predicting the values of binary decision variables …

We introduce the idea that using optimal classification trees (OCTs) and optimal
classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms …

This survey gives a selective review of recent development of machine learning (ML) for
combinatorial optimization (CO), especially for graph matching. The synergy of these two …

Many robotics problems, from robot motion planning to object manipulation, can be modeled
as mixed-integer convex program (MICPs). However, state-of-the-art algorithms are still …

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing
and computation capabilities in order to detect anomalies and apply autonomous event …

We introduce a prescriptive machine learning approach to speed up the process of solving
mixed-integer convex optimization (MICO) problems. We solve multiple optimization …