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 …

Better forecasts may not lead to better decision-making. To address this challenge, decision-
oriented learning (DOL) has been proposed as a new branch of machine learning that …

Optimization layers in deep neural networks have enjoyed a growing popularity in structured
learning, improving the state of the art on a variety of applications. Yet, these pipelines lack …

The end-to-end predict-then-optimize framework, also known as decision-focused learning,
has gained popularity for its ability to integrate optimization into the training procedure of …

Dealing with uncertainty in optimization parameters is an important and longstanding
challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic …

Data-driven optimization often involves the prediction of uncertain parameters drawn from
unknown probability distributions for a subsequent optimization task. Recent literature has …

Optimization layers in deep neural networks have enjoyed a growing popularity in structured
learning, improving the state of the art on a variety of applications. Yet, these pipelines lack …

The aspiration to use artificial intelligence (AI) in decision-making has been growing rapidly
in recent years. In most real-world applications, the optimal decisions not only optimize …

Lidiar con parámetros desconocidos en problemas de optimización es un desafío
importante y ha sido bien estudiado en la literatura. Una forma de enfrentar este problema …

Recently, machine learning has revolutionized optimization. The" predict-then-optimize"
approach is gaining attention globally. But there is a noticeable absence of this approach in …