This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …

Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …

Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …

We consider the use of decision trees for decision-making problems under the predict-then-
optimize framework. That is, we would like to first use a decision tree to predict unknown …

Solving optimization problem is the key to decision making in many real-life analytics
applications. However, the coefficients of the optimization problems are often uncertain and …

Building on recent progress at the intersection of combinatorial optimization and deep
learning, we propose an end-to-end trainable architecture for deep graph matching that …

Bridging logical and algorithmic reasoning with modern machine learning techniques is a
fundamental challenge with potentially transformative impact. On the algorithmic side, many …

In the last years decision-focused learning framework, also known as predict-and-optimize,
have received increasing attention. In this setting, the predictions of a machine learning …

Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …