Bridging Optimal Transport, Learning and Structured Data: Toward Geometric Distributional Learning

Modern machine learning increasingly relies on both geometric and distributional representations of complex data. While geometric deep learning provides tools to encode symmetries, invariances and relational structure in non-Euclidean domains, distributional methods, including optimal transport, offer principled ways to compare align, and transform data distributions. These perspectives are deeply connected but often developed separately. This workshop will focus on the emerging area of Geometric Distributional Deep Learning: learning systems that jointly model the geometry and distributional nature of data, features or representations. The goal is to bring together researchers from geometric deep learning, computational optimal transport, generative modeling, and representation learning to discuss how geometric and distributional principles can inform new architectures, algorithms and applications for structured data.