Geometrical and Topological Representation Learning

Over the past two decades, high-throughput data collection technologies have become commonplace in most fields of science and technology, and with them an ever-increasing amount of big high dimensional data is being generated by virtually every real-world system. While such data systems are highly diverse in nature, the underlying data analysis and exploration tasks give rise to common challenges at the core of modern representation learning. For example, even though modern real-world data typically exhibit high-dimensional ambient measurement spaces, they often exhibit low-dimensional intrinsic structures that can be uncovered by geometry-oriented methods, such as the ones encountered in manifold learning, graph signal processing, geometric deep learning, and topological data analysis. As a result, recent years have seen significant interest and progress in geometric and topological approaches to representation learning, thus enabling tractable exploratory analysis by domain experts who frequently do not have a strong computational background.Motivation. Despite increased interest in the aforementioned methods, there is no forum in which to present work in progress to get the feedback of the machine learning community. Knowing the diverse backgrounds of researchers visiting ICLR, we consider this venue to be the perfect opportunity to bring together domain experts, practitioners, and researchers that are developing the next-generation computational methods. In our opinion, such discussions need to be held in an inclusive setting, getting feedback from different perspectives to improve the work and advance the state of the art. Our workshop provides a unique forum for disseminating (preliminary) research in fields that are not yet fully covered by the main conference. Our overarching goal is to deepen our understanding of challenges/opportunities, while breaking barriers between disjoint communities, emphasizing collaborative efforts in different domains.