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 …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Recent work has shown how to embed differentiable optimization problems (that is,
problems whose solutions can be backpropagated through) as layers within deep learning …

We present a differentiable physics engine that can be integrated as a module in deep
neural networks for end-to-end learning. As a result, structured physics knowledge can be …

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 …

We propose a new class of implicit networks, the multiscale deep equilibrium model
(MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. An …

Abstract Machine learning pipelines often rely on optimizers procedures to make discrete
decisions (eg, sorting, picking closest neighbors, or shortest paths). Although these discrete …