Abstract: Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical ...

This repository contains a refactoring of the code used in the paper "Learning Latent Graph Structures and Their Uncertainty" (ICML 2025). The code is designed to be modular and easy to use, allowing ...

Discrete structures are omnipresent in mathematics, computer science, statistical physics, optimisation and models of natural phenomena. For instance, complex random graphs serve as a model for social ...

*Note: This course description is only applicable for the Computer Science Post-Baccalaureate program. Additionally, students must always refer to course syllabus for the most up to date information.

Abstract: Discrete decision-making problems, such as combinatorial optimization tasks frequently encountered in network science and industrial IoT systems, are pervasive in real-world applications.