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Poster
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Cristopher Salvi · Maud Lemercier · Chong Liu · Blanka Horvath · Theodoros Damoulas · Terry Lyons

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
in Poster Session 1 »

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. the flow of information carried by the process through time. By conditioning the process on its filtration, we introduce a family of higher order kernel mean embeddings (KMEs) that generalizes the notion of KME to capture additional information related to the filtration. We derive empirical estimators for the associated higher order maximum mean discrepancies (MMDs) and prove consistency. We then construct a filtration-sensitive kernel two-sample test able to capture information that gets missed by the standard MMD test. In addition, leveraging our higher order MMDs we construct a family of universal kernels on stochastic processes that allows to solve real-world calibration and optimal stopping problems in quantitative finance (such as the pricing of American options) via classical kernel-based regression methods. Finally, adapting existing tests for conditional independence to the case of stochastic processes, we design a causal-discovery algorithm to recover the causal graph of structural dependencies among interacting bodies solely from observations of their multidimensional trajectories.

Keywords: Machine Learning · Graph Learning · Causality

Author Information

Cristopher Salvi (University of Oxford)
Maud Lemercier (University of Warwick)
Chong Liu (University of Oxford)
Blanka Horvath (Technical University Munich)
Theodoros Damoulas (University of Warwick)
Terry Lyons (University of Oxford)

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