Publication
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng; Florian Peter Busch; Pooja Prasad; Devendra Singh Dhami; Martin Mundt; Kristian Kersting
In: Silvia Chiappa; Sara Magliacane (Hrsg.). Conference on Uncertainty in Artificial Intelligence, Rio Othon Palace, Rio de Janeiro, Brazil, 21-25 July 2025. International Conference on Uncertainty in AI (UAI), Pages 3701-3717, Proceedings of Machine Learning Research, Vol. 286, PMLR, 2025.
Abstract
Probabilistic circuits (PCs) enable us to learn joint
distributions over a set of random variables and to
perform various probabilistic queries in a tractable
fashion. Though the tractability property allows
PCs to scale beyond non-tractable models such as
Bayesian Networks, scaling training and inference
of PCs to larger, real-world datasets remains chal-
lenging. To remedy the situation, we show how
PCs can be learned across multiple machines by re-
cursively partitioning a distributed dataset, thereby
unveiling a deep connection between PCs and fed-
erated learning (FL). This leads to federated cir-
cuits (FCs)—a novel and flexible federated learn-
ing (FL) framework that (1) allows one to scale
PCs on distributed learning environments (2) train
PCs faster and (3) unifies for the first time horizon-
tal, vertical, and hybrid FL in one framework by
re-framing FL as a density estimation problem over
distributed datasets. We demonstrate FC’s capabil-
ity to scale PCs on various large-scale datasets.
Also, we show FC’s versatility in handling hor-
izontal, vertical, and hybrid FL within a unified
framework on multiple classification tasks.