Raghav Kansal — accepted AI-conference papers

2026

Multimarginal flow matching with optimal transport potentials

Raghav Kansal, David Crair, Nghia Nguyen, Scott Pope, Bradley Parry

ICML 2026poster

Flow matching (FM) has emerged as a powerful framework for learning dynamic transport maps between two empirical distributions. However, less explored is the setting with intermediate observed marginals that can help constrain the flows between the endpoints. This "multimarginal" regime is central t…

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2021

Particle Cloud Generation with Message Passing Generative Adversarial Networks

Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago R F P Tomei, Maurizio Pierini +3

NeurIPS 2021poster

In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generative adversarial networks (GANs), have the potential to signific…

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