Maud Lemercier: Non-adversarial training of Neural SDEs with signature kernel scores


Neural SDEs are continuous-time generative models for sequential data. State-of-the-art performance for irregular time series generation has been previously obtained by training these models adversarially as GANs. However, as typical for GAN architectures, training is notoriously unstable, often suffers from mode collapse, and requires specialised techniques such as weight clipping and gradient penalty to mitigate these issues. In this talk, I will introduce a novel class of scoring rules on path space based on signature kernels and use them as an objective for training Neural SDEs non-adversarially. The strict properness of such kernel scores and the consistency of the corresponding estimators, provide existence and uniqueness guarantees for the minimiser. With this formulation, evaluating the generator-discriminator pair amounts to solving a system of linear path-dependent PDEs which allows for memory-efficient adjoint-based backpropagation. Moreover, because the proposed kernel scores are well-defined for paths with values in infinite-dimensional spaces of functions, this framework can be easily extended to generate spatiotemporal data. This procedure permits conditioning on a rich variety of market conditions and significantly outperforms alternative ways of training Neural SDEs on a variety of tasks including the simulation of rough volatility models, the conditional probabilistic forecasts of real-world forex pairs where the conditioning variable is an observed past trajectory, and the mesh-free generation of limit order book dynamics.


I am currently a postdoctoral researcher at the Mathematical Institute of the University of Oxford. Before this, I completed my PhD at the University of Warwick as part of the Oxford-Warwick Statistics programme (2018-2022). My research focuses on developing machine learning techniques for sequential data analysis, drawing on mathematical insights and tools from rough path theory. In this context, I am working on kernel-based algorithms and outlier detection techniques tailored for high-frequency data streams. I am particularly interested in molecular biology challenges, especially those involving high-throughput RNA sequencing data.
Marc Deisenroth
Marc Deisenroth
Google DeepMind Chair of Machine Learning and Artificial Intelligence