A Short Note on the Sample Complexity of Learning Probabilistic Circuits
Published in Preliminary Result, 2025
Publications
On the Hardness of Approximating Distributions with Tractable Probabilistic Models
Published in NeurIPS, 2025
TLDR: We investigate the hardness of approximating distributions with TPMs. Our main results show the NP-hardness of approximation with a bounded f-divergence for any model which can tractably marginalize, and an exponential lower bound on the size of a deterministic, decomposable probabilisitic circuit representing a distribution exactly encoded by a decomposable probabilistic circuit. We also have a number of results related to bounded f-divergence and approximate inference guarantees.
Fair Image Generation from Pre-trained Models by Probabilistic Modeling
Published in In the Safe Generative AI Workshop at NeurIPS, 2024