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 …

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …

In deterministic optimization, it is typically assumed that all problem parameters are fixed
and known. In practice, however, some parameters may be a priori unknown but can be …

Recent works in learning-integrated optimization have shown promise in settings where the
optimization problem is only partially observed or where general-purpose optimizers …

Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful
tool to tackle data-driven decision tasks, but they come with two main challenges. First, the …

Marketing is an important mechanism to increase user engagement and improve platform
revenue, and heterogeneous causal learning can help develop more effective strategies …

In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an
increasing interest in neural architectures that can learn how to solve discrete reasoning or …

Abstract Machine learning (ML) has attracted recent attention in solving constrained
optimization problems to reduce computational time. In this article, we employ multi-output …

This survey explores the integration of learning and reasoning in two different fields of
artificial intelligence: neurosymbolic and statistical relational artificial intelligence …

In this paper, we develop an integrated estimation-and-optimization framework for
constructing decisions rules for the order quantities of multiple perishable products. The …