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stat.ML2026
Learning interacting particle systems from unlabeled data
Viska Wei, Fei Lu
Learning the potentials of interacting particle systems is a fundamental task across various scientific disciplines. A major challenge is that unlabeled data collected at discrete…
stat.ML2026
Learning Multi-type heterogeneous interacting particle systems
Quanjun Lang, Xiong Wang, Fei Lu +1
We propose a framework for the joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems from m…