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 …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

This paper focuses on training implicit models of infinite layers. Specifically, previous works
employ implicit differentiation and solve the exact gradient for the backward propagation …

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 …

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

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

Prediction and optimization are the foundation of many real-world analytics problems in
various disciplines. As both can be challenging, they are usually treated sequentially in …

This work proposes a new unsupervised (or self-supervised) node representation learning
method that aims to leverage the coarse-grain information that is available in most graphs …

The success of deep learning (DL) based-mechanical fault diagnosis hinges on the high
quality of training data. However, it is difficult to acquire high-quality mechanical monitoring …