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Francisco Tellez

francisco.jose.tellez@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Guy Wolf
Sujets de recherche
Exploration des données
Géométrie des données

Publications

Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Tellez
Amirhossein Zamani
Philippe Martin
Shuang Ni
Guy Wolf
Eugene Belilovsky
Sina Sanjari
Yanlei Zhang
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formu… (voir plus)lation is not designed to capture multi-parameter variations where the resulting transport should be path-independent. Path independence is crucial because it ensures that transformations depend only on the initial and target distributions, not on the specific path. In this work, we introduce Path-independent Flow Matching (PiFM), a method for learning vector fields whose induced flows yield path-independent transport between distributions. We show that PiFM generalizes Flow Matching to higher-dimensional parameter domains while enforcing structural conditions that ensure consistency of composed transformations. In addition, we show that, under suitable assumptions, PiFM approximates the Wasserstein barycenter, linking the framework to a notion of distributional interpolation. To enable practical training, we propose a tractable, simulation-free objective that regresses onto multi-parameter conditional probability paths. We showcase empirically that PiFM outperforms other approaches on both synthetic and real world data in interpolating path-independent trajectories and generating desired out of distribution samples.
2026-05-29
SPIGM @ International Conference on Machine Learning (poster)
doi.org
openreview.net