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 …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

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

Many meta-learning approaches for few-shot learning rely on simple base learners such as
nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

This paper presents OptNet, a network architecture that integrates optimization problems
(here, specifically in the form of quadratic programs) as individual layers in larger end-to-end …

Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …

Counterfactual explanations are emerging as an attractive option for providing recourse to
individuals adversely impacted by algorithmic decisions. As they are deployed in critical …