Introduction
Join the Rough Paths Interest Group for a workshop showcasing the latest advancements in integrating rough path theory with rapidly evolving field of deep learning. A central theme will be the signature of a path—an important mathematical tool for capturing the essence of complex, irregular data—and its broad applications across various domains.
About the event
The event program will explore how these techniques can be leveraged to enhance models in control system design, sequence modelling, and time series generation. Additionally, this workshop will also feature an introduction of Kolmogorov-Arnold networks and demonstrate their effectiveness as a versatile framework for time series analysis. Attendees will gain a deeper understanding of how rough path theory and signature-based methods can provide solutions to challenges in data science.
The event will take place in-person at The Alan Turing Institute.
Agenda
11:15-11:30 Welcome
11:30-12:15 On the role of the signature in control design, Anna Scampicchio, ETH Zurich
12:15-13:00 Score-Based Diffusion for Generating Paths via Signature Embeddings, Barbora Barancikova, Imperial College London
13:00-14:00 Lunch
14:00-14:45 Unravelling Kolmogorov Arnold Networks, Hugo Inzirillo, CREST, Institut Polytechnique de Paris
14:45-15:30 Time Series Analysis with Signature-Weighted Kolmogorov-Arnold Networks, Rémi Genet, Paris Dauphine University
15:30-16:15 Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures, Fernando Moreno-Pino, OMI, University of Oxford
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