| Covariances, robustness, and variational Bayes R Giordano, T Broderick, MI Jordan Journal of machine learning research 19 (51), 1-49, 2018 | 120 | 2018 |
| A swiss army infinitesimal jackknife R Giordano, W Stephenson, R Liu, M Jordan, T Broderick The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 99 | 2019 |
| Linear response methods for accurate covariance estimates from mean field variational Bayes RJ Giordano, T Broderick, MI Jordan Advances in neural information processing systems 28, 2015 | 99 | 2015 |
| An automatic finite-sample robustness metric: Can dropping a little data change conclusions T Broderick, R Giordano, R Meager arXiv preprint arXiv:2011.14999 16 (1), 2, 2020 | 61* | 2020 |
| Cataloging the visible universe through Bayesian inference in Julia at petascale J Regier, K Fischer, K Pamnany, A Noack, J Revels, M Lam, S Howard, ... Journal of Parallel and Distributed Computing 127, 89-104, 2019 | 41 | 2019 |
| The mind, the lab, and the field: three kinds of populations in scientific practice RG Winther, R Giordano, MD Edge, R Nielsen Studies in History and Philosophy of Science Part C: Studies in History and …, 2015 | 34 | 2015 |
| A higher-order swiss army infinitesimal jackknife R Giordano, MI Jordan, T Broderick arXiv preprint arXiv:1907.12116, 2019 | 26 | 2019 |
| Evaluating sensitivity to the stick-breaking prior in bayesian nonparametrics (with discussion) R Giordano, R Liu, MI Jordan, T Broderick Bayesian Analysis 18 (1), 287-366, 2023 | 13 | 2023 |
| How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences MJ Kasprzak, R Giordano, T Broderick arXiv preprint arXiv:2209.14992, 2022 | 11 | 2022 |
| Learning an astronomical catalog of the visible universe through scalable Bayesian inference J Regier, K Pamnany, R Giordano, R Thomas, D Schlegel, J McAuliffe arXiv preprint arXiv:1611.03404, 2016 | 9 | 2016 |
| Fast robustness quantification with variational Bayes R Giordano, T Broderick, R Meager, J Huggins, M Jordan arXiv preprint arXiv:1606.07153, 2016 | 9 | 2016 |
| Return of the infinitesimal jackknife R Giordano, W Stephenson, R Liu, MI Jordan, T Broderick arXiv preprint arXiv:1806.00550, 2018 | 6 | 2018 |
| Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box R Giordano, M Ingram, T Broderick Journal of Machine Learning Research 25 (18), 1-39, 2024 | 4 | 2024 |
| Gaussian processes at the Helm (holtz): A more fluid model for ocean currents R Berlinghieri, BL Trippe, DR Burt, R Giordano, K Srinivasan, ... arXiv preprint arXiv:2302.10364, 2023 | 3 | 2023 |
| Robust inference with variational bayes R Giordano, T Broderick, M Jordan arXiv preprint arXiv:1512.02578, 2015 | 3 | 2015 |
| Covariance matrices and influence scores for mean field variational bayes R Giordano, T Broderick arXiv preprint arXiv:1502.07685, 2015 | 3 | 2015 |
| On the Local Sensitivity of M-Estimation: Bayesian and Frequentist Applications R Giordano University of California, Berkeley, 2019 | 2 | 2019 |
| The Bayesian Infinitesimal Jackknife for Variance R Giordano, T Broderick arXiv preprint arXiv:2305.06466, 2023 | 1 | 2023 |
| Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics R Liu, R Giordano, MI Jordan, T Broderick arXiv e-prints, arXiv: 1810.06587, 2018 | 1 | 2018 |
| Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap R Giordano, R Liu, N Varoquaux, MI Jordan, T Broderick arXiv preprint arXiv:1712.01435, 2017 | 1 | 2017 |
Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyVerified email at mit.edu
