50 citations · 159 across the 31 of their papers we have counts for
5 papers · 1 filter
Novelty detection on path space
Ioannis Gasteratos, Antoine Jacquier, Maud Lemercier +2
We frame novelty detection on path space as a hypothesis testing problem with signature-based test statistics. Using transportation-cost inequalities of Gasteratos and Jacquier (20…
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
Christian Holberg, Cristopher Salvi
We introduce a mathematically rigorous framework based on rough path theory to model stochastic spiking neural networks (SSNNs) as stochastic differential equations with event disc…
Non-adversarial training of Neural SDEs with signature kernel scores
Zacharia Issa, Blanka Horvath, Maud Lemercier +1
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 th…
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Cristopher Salvi, Maud Lemercier, Chong Liu +3
Stochastic processes are random variables with values in some space of paths. However, reducing a stochastic process to a path-valued random variable ignores its filtration, i.e. t…
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass +3
Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention. We develop Sig…