3 citations · 3 across the 3 of their papers we have counts for
5 papers
A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Vincent Guan, Lazar Atanackovic, Kirill Neklyudov
The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynamics have predominantly been modeled with…
Gradient-flow SDEs have unique transient population dynamics
Vincent Guan, Joseph Janssen, Nicolas Lanzetti +3
Identifying the drift and diffusion of an SDE from its population dynamics is a notoriously challenging task. Researchers in machine learning and single-cell biology have only been…
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
Vincent Guan, Joseph Janssen, Hossein Rahmani +4
Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets…
Path-Dependent SDEs: Solutions and Parameter Estimation
Pardis Semnani, Vincent Guan, Elina Robeva +1
We develop a consistent method for estimating the parameters of a rich class of path-dependent SDEs, called signature SDEs, which can model general path-dependent phenomena. Path s…
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
Joseph Janssen, Vincent Guan, Elina Robeva
Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal contr…