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 …

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

Large optimization problems with hard constraints arise in many settings, yet classical
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …

Integrating logical reasoning within deep learning architectures has been a major goal of
modern AI systems. In this paper, we propose a new direction toward this goal by …

Creating impact in real-world settings requires artificial intelligence techniques to span the
full pipeline from data, to predictive models, to decisions. These components are typically …

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

We explore a class of end-to-end learnable models wherein data processing nodes (or
network layers) are defined in terms of desired behavior rather than an explicit forward …

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

When designing controllers for safety-critical systems, practitioners often face a challenging
tradeoff between robustness and performance. While robust control methods provide …

Abstract Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a
downstream optimization task that uses its predictions in order to perform better\textit {on that …