5 papers
Learning Lineage-guided Geodesics with Finsler Geometry
Aaron Zweig, Mingxuan Zhang, David A. Knowles +1
Trajectory inference investigates how to interpolate paths between observed timepoints of dynamical systems, such as temporally resolved population distributions, with the goal of…
Towards Identifiability of Interventional Stochastic Differential Equations
Aaron Zweig, Zaikang Lin, Elham Azizi +1
We study identifiability of stochastic differential equations (SDE) under multiple interventions. Our results give the first provable bounds for unique recovery of SDE parameters g…
Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
Zaikang Lin, Sei Chang, Aaron Zweig +4
Modern high-throughput biological datasets containing thousands of perturbations enable large-scale discovery of causal graphs that represent regulatory interactions between genes.…
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili +1
Observational causal discovery is only identifiable up to the Markov equivalence class. While interventions can reduce this ambiguity, in practice interventions are often soft with…
Energy Guided Geometric Flow Matching
Aaron Zweig, Mingxuan Zhang, Elham Azizi +1
A useful inductive bias for temporal data is that trajectories should stay close to the data manifold. Traditional flow matching relies on straight conditional paths, and flow matc…