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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.

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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