11 citations · 33 across the 12 of their papers we have counts for
12 papers
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…
Path Signatures Enable Model-Free Mapping of RNA Modifications
Maud Lemercier, Paola Arrubarrena, Salvatore Di Giorgio +10
Detecting chemical modifications on RNA molecules remains a key challenge in epitranscriptomics. Traditional reverse transcription-based sequencing methods introduce enzyme- and se…
Log-PDE Methods for Rough Signature Kernels
Maud Lemercier, Terry Lyons, Cristopher Salvi
Signature kernels, inner products of path signatures, underpin several machine learning algorithms for multivariate time series analysis. For bounded variation paths, signature ker…
Novelty Detection on Radio Astronomy Data using Signatures
Paola Arrubarrena, Maud Lemercier, Bojan Nikolic +2
We introduce SigNova, a new semi-supervised framework for detecting anomalies in streamed data. While our initial examples focus on detecting radio-frequency interference (RFI) in…
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…
Optimal Stopping via Distribution Regression: a Higher Rank Signature Approach
Blanka Horvath, Maud Lemercier, Chong Liu +2
Distribution Regression on path-space refers to the task of learning functions mapping the law of a stochastic process to a scalar target. The learning procedure based on the notio…
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi
Motivated by the paradigm of reservoir computing, we consider randomly initialized controlled ResNets defined as Euler-discretizations of neural controlled differential equations (…
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics
Cristopher Salvi, Maud Lemercier, Andris Gerasimovics
Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling spatiotemporal PDE-dynamics under the influence of randomness. Based on the noti…
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…
SK-Tree: a systematic malware detection algorithm on streaming trees via the signature kernel
Thomas Cochrane, Peter Foster, Varun Chhabra +3
The development of machine learning algorithms in the cyber security domain has been impeded by the complex, hierarchical, sequential and multimodal nature of the data involved. In…
Distribution Regression for Sequential Data
Maud Lemercier, Cristopher Salvi, Theodoros Damoulas +2
Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs. In this paper, we develop a rig…