Olga Zaghen — accepted AI-conference papers

2026

Riemannian Variational Flow Matching for Material and Protein Design

Olga Zaghen, Floor Eijkelboom, Alison Pouplin, Cong Liu, Max Welling, Jan-Willem van de Meent +1

ICLR 2026poster

We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the benefits of VFM, we derive a variational flow matching objective for manifolds with closed-form geodesics based on Rieman…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Revisiting Random Walks for Learning on Graphs

Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade, Youngmin Ryou, Seunghoon Hong

ICLR 2025spotlight

We revisit a simple model class for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural network to directly make vertex-level or graph-level predictions. We call these stochastic machines random walk neural netw…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗