1 on Diana Cai https://dicai.github.io/publication_types/1/ Recent content in 1 on Diana Cai Hugo -- gohugo.io en-us Fri, 17 Oct 2025 00:00:00 +0000 Fisher meets Feynman: score-based variational inference with a product of experts https://dicai.github.io/publication/conference-paper/2025-fisher-meets-feynman/ Fri, 17 Oct 2025 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2025-fisher-meets-feynman/ Batch, match, and patch: low-rank approximations for score-based variational inference https://dicai.github.io/publication/conference-paper/2024-patched-bam-bbvi/ Fri, 10 Jan 2025 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2024-patched-bam-bbvi/ EigenVI: score-based variational inference with orthogonal function expansions https://dicai.github.io/publication/conference-paper/2024-eigenvi-score-based-variational-inference/ Thu, 31 Oct 2024 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2024-eigenvi-score-based-variational-inference/ Batch and match: black-box variational inference using a score-based divergence https://dicai.github.io/publication/conference-paper/2024-batch-and-match-gaussian-bbvi/ Mon, 22 Jul 2024 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2024-batch-and-match-gaussian-bbvi/ Multi-fidelity Monte Carlo: a pseudo-marginal approach https://dicai.github.io/publication/conference-paper/2022-multi-fidelity-mcmc/ Mon, 05 Dec 2022 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2022-multi-fidelity-mcmc/ Citation @inproceedings{cai2022multi, title={Multi-fidelity Monte Carlo: a pseudo-marginal approach}, author={Diana Cai and Ryan P Adams}, booktitle={Advances in Neural Information Processing Systems 35}, year={2022} } Slice sampling reparameterization gradients https://dicai.github.io/publication/conference-paper/2021-reparameterized-gradients-slice-sampling/ Tue, 28 Sep 2021 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2021-reparameterized-gradients-slice-sampling/ Preliminary version appeared in the AABI Symposium in January 2021. Citation @inproceedings{zoltowski2021slice, title={Slice Sampling Reparameterization Gradients}, author={David M. Zoltowski and Diana Cai and Ryan P Adams}, booktitle={Advances in Neural Information Processing Systems 34}, year={2021} } Finite mixture models do not reliably learn the number of components 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} } Active multi-fidelity Bayesian online changepoint detection https://dicai.github.io/publication/conference-paper/2021-active-multi-fidelity-bayesian-changepoint-detection/ Mon, 01 Mar 2021 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2021-active-multi-fidelity-bayesian-changepoint-detection/ Citation @article{gundersen2021active, title={Active multi-fidelity Bayesian online changepoint detection}, author={Gundersen, Gregory W and Cai, Diana and Zhou, Chuteng and Engelhardt, Barbara E and Adams, Ryan P}, journal={arXiv preprint arXiv:2103.14224}, year={2021} } A Bayesian nonparametric view on count-min sketch https://dicai.github.io/publication/conference-paper/2018-cmsketch/ Sat, 01 Dec 2018 00:00:00 +0000 https://dicai.github.io/publication/conference-paper/2018-cmsketch/ Citation @inproceedings{cai2018bayesian, title={A Bayesian nonparametric view on count-min sketch}, author={Cai, Diana and Mitzenmacher, Michael and Adams, Ryan P}, booktitle={Advances in Neural Information Processing Systems 31}, pages={8782--8791}, year={2018} } Edge-exchangeable graphs and sparsity 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 …