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 …

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited
annotated samples has remained an open challenge for medical imaging data. While pre …

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

The sorting operation is one of the most commonly used building blocks in computer
programming. In machine learning, it is often used for robust statistics. However, seen as a …

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently,
provably boosting the expressive power of standard (message-passing) GNNs. However …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing
complex pairwise-distance losses, which require convoluted schemes to ease optimization …

Detecting edges in images suffers from the problems of (P1) heavy imbalance between
positive and negative classes as well as (P2) label uncertainty owing to disagreement …

Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common metrics for
evaluating classification performance for imbalanced problems. Compared with AUROC …

Building on recent progress at the intersection of combinatorial optimization and deep
learning, we propose an end-to-end trainable architecture for deep graph matching that …