Robert Peharz

I'm happy to share that our paper "Bayesian Structure Scores for Probabilistic Circuits" has been accepted at #aistats23! This is joint work with Yang Yang (MSc @mastodon.world, incoming PhD @leuvenai and Gennaro Gala.

The main contribution of the paper is to propose a new (actually old) way to learn the structure of #ProbabilisticCircuits. We take lessons from classical structure learning in Bayesian networks which optimizes some structure score, where a principled choice is a Bayesian score.

Jan 23, 2023 at 1:01pmWeb
Show threadRobert PeharzJan 23, 2023
In short, a Bayesian structure score is simply the marginal likelihood of the structure G, integrating over model parameters θ: p(data | G) = ∫ p(data | G, θ) p(θ | G) dθ. Integrating over all parameterizations θ effectively protects against overfitting. A cool result is that, under certain assumptions, this score can be computed in closed form in Bayesian networks, see e.g. the classical paper by Heckerman, Geiger and Chickering, (1995).
Show threadRobert PeharzJan 23, 2023

The same score can be derived for probabilistic circuits (PCs), or actually, for the sub-class of deterministic PCs! We use the score in a simple cutset learning algorithm and achieve excellent trade-offs between learning speed and test log-likelihoods.

Stay tuned for the paper, or catch up with us at #aistats23 in April! 😀

Show threadMartin TrappJan 23, 2023
@rope @leuvenai Nice, congrats.