50 citations · 159 across the 31 of their papers we have counts for
5 papers · 1 filter
A Neural RDE approach for continuous-time non-Markovian stochastic control problems
Melker Hoglund, Emilio Ferrucci, Camilo Hernandez +4
We propose a novel framework for solving continuous-time non-Markovian stochastic control problems by means of neural rough differential equations (Neural RDEs) introduced in Morri…
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 (…
New directions in the applications of rough path theory
Adeline Fermanian, Terry Lyons, James Morrill +1
This article provides a concise overview of some of the recent advances in the application of rough path theory to machine learning. Controlled differential equations (CDEs) are di…