Neural Structure Learning with Stochastic Differential Equations

arXiv:2311.03309v2 Announce Type: replace
Abstract: Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance, and climate science. The dynamics of such systems are often best described using continuous-time stochastic processes. Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals. These mismatched assumptions can often lead to incorrect learned structures and models. In this work, we introduce …

abstract arxiv biology challenge climate climate science continuous cs.ai cs.lg differential dynamics finance process processes relationships science scientific stat.ml stochastic systems temporal type variables