gala2023pic update accepted

_news/gala2023pic-accepted.md

@@ -0,0 +1,7 @@
+---
+title: "Probabilistic Integral Circuits"
+collection: news
+permalink: /news/pic-accepted
+date: 2024-01-19
+---
+Our work on <a href="https://arxiv.org/abs/2310.16986"><b>tractable continuous latent variable models</b></a>  in the language of circuits in collaboration with <a href="https://uai.win.tue.nl/#people">UAI@TU/e</a> is accepted at <b>AISTATS24</b>.

_publications/gala2023pic.md

@@ -3,21 +3,21 @@ collection: publications
 ref: "gala2023pic"
 permalink: "publications/gala2023pic"
 title:  "Probabilistic Integral Circuits"
-date:   2023-10-28 00:00
+date:   2024-01-19 00:00
 tags: circuits probml
 image: "/images/papers/gala2023pic/pic-comp.png"
 authors: "Gennaro Gala, Cassio de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur"
 paperurl: "https://arxiv.org/abs/2310.16986"
 pdf: "https://arxiv.org/pdf/2310.16986.pdf"
-venue: "arXiv 2023"
+venue: "AISTATS 2024"
 code: "https://github.com/gengala/pic"
 excerpt: "We represent hierarchical continuous latent variable models as computational graph that allow us to efficiently approximate intractable distributions via recursive quadrature rules"
 abstract: "Continuous latent variables (LVs) are a key ingredient of many generative models, as they allow modelling expressive mixtures with an uncountable number of components. In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units. Unlike continuous LV models, PCs provide tractable inference but are limited to discrete LVs with categorical (i.e. unordered) states. We bridge these model classes by introducing probabilistic integral circuits (PICs), a new language of computational graphs that extends PCs with integral units representing continuous LVs. In the first place, PICs are symbolic computational graphs and are fully tractable in simple cases where analytical integration is possible. In practice, we parameterise PICs with light-weight neural nets delivering an intractable hierarchical continuous mixture that can be approximated arbitrarily well with large PCs using numerical quadrature. On several distribution estimation benchmarks, we show that such PIC-approximating PCs systematically outperform PCs commonly learned via expectation-maximization or SGD."
 supplemental: 
-bibtex: "@article{gala2023pic,<br/>
+bibtex: "@inproceedings{gala2023pic,<br/>
   title={Probabilistic Integral Circuits},<br/>
   author={Gennaro Gala, Cassio de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur,<br/>
-  journal={arXiv preprint arXiv:2310.16986},<br/>
-  year={2023}
+  booktitle={AISTATS 2024},<br/>
+  year={2024}
 }"
 ---