https://dicai.github.io/authors/trevor-campbell/ Recent content in Trevor Campbell on Diana Cai Hugo -- gohugo.io en-us Thu, 01 Jul 2021 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2021-finite-mixtures-unreliable/ Thu, 01 Jul 2021 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2021-finite-mixtures-unreliable/ Preliminary versions appeared in NeurIPS 2019 Workshop on Machine Learning with Guarantees and also the Symposium on Advances in Approximate Bayesian Inference 2017, co-located with NeurIPS 2017. Citation @InProceedings{pmlr-v139-cai21a, title = {Finite mixture models do not reliably learn the number of components}, author = {Cai, Diana and Campbell, Trevor and Broderick, Tamara}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {1158--1169}, year = {2021}, volume = {139}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR} } https://dicai.github.io/publication/conference-paper/2020-power-mix/ Thu, 08 Oct 2020 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2020-power-mix/ See also: Finite mixture models do not reliably learn the number of components (arXiv e-print 2007.04470) for results on learning the number of components in a finite mixture model via the usual posterior distribution. https://dicai.github.io/publication/conference-paper/2019-finmix/ Sun, 01 Dec 2019 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2019-finmix/ https://dicai.github.io/publication/journal-article/2018-traitexch/ Sat, 01 Dec 2018 00:00:00 +0000 https://dicai.github.io/publication/journal-article/2018-traitexch/ Trait allocations are a class of combinatorial structures in which data may belong to multiple groups and may have different levels of belonging in each group. Often the data are also exchangeable, i.e., their joint distribution is invariant to reordering. In clustering—a special case of trait allocation—exchangeability … https://dicai.github.io/publication/conference-paper/2017-finmix/ Fri, 01 Dec 2017 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2017-finmix/ https://dicai.github.io/publication/conference-paper/2016-edgeexch/ Thu, 01 Dec 2016 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2016-edgeexch/ Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that these graphs are dense or empty with probability one, whereas many real-world graphs are sparse … https://dicai.github.io/publication/conference-paper/2016-paintbox/ Thu, 01 Dec 2016 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2016-paintbox/ In NeurIPS 2016 Workshop on Adaptive and Scalable Nonparametric Methods in Machine Learning