Publications
For preprints, see my arxiv page.
Giordano, R., and T. Broderick. 2025. “The Bayesian Infinitesimal Jackknife for Variance.” Journal of the American Statistical Association.
Giordano, R., R. Meager, and T. Broderick. 2025a. “An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? Part I: Definitions and Experiments.” Philosophical Transactions of the Royal Society A.
Giordano, R., R. Meager, and T. Broderick. 2025b. “An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? Part II: Theory and Intuition.” Philosophical Transactions of the Royal Society A.
Kasprzak, M., R. Giordano, and T. Broderick. 2025. “How Good Is Your Laplace Approximation of the Bayesian Posterior? Finite-sample Computable Error Bounds for a Variety of Useful Divergences.” Journal of Machine Learning Research 26 (87): 1–81.
Giordano, R., M. Ingram, and T. Broderick. 2024. “Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box.” Journal of Machine Learning Research 25 (18): 1–39.
Berlinghieri, R., B. Trippe, D. Burt, et al. 2023. “Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents.” Proceedings of the 40th International Conference on Machine Learning, Proceedings of machine learning research.
Giordano, R., R. Liu, M. I. Jordan, and T. Broderick. 2023. “Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (with Discussion).” Bayesian Analysis 18 (1): 287–366.
Giordano, R., W. Stephenson, R. Liu, M. I. Jordan, and T. Broderick. 2019. “A Swiss Army Infinitesimal Jackknife.” The 22nd International Conference on Artificial Intelligence and Statistics, 1139–47.
Giordano, R., T. Broderick, and M. I. Jordan. 2018. “Covariances, Robustness, and Variational Bayes.” Journal of Machine Learning Research 19 (51): 1–49. http://jmlr.org/papers/v19/17-670.html.
Regier, J., K. Pamnany, K. Fischer, et al. 2018. “Cataloging the Visible Universe Through Bayesian Inference at Petascale.” 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 44–53.
Giordano, R., T. Broderick, and M. I. Jordan. 2015. “Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes.” Advances in Neural Information Processing Systems, 1441–49.
Winther, R., R. Giordano, M. Edge, and R. Nielsen. 2015. “The Mind, the Lab, and the Field: Three Kinds of Populations in Scientific Practice.” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 52: 12–21.
Slides from Selected Presentations
- MrPlew (Presented at 2025 Berkeley / Stanford joint colloquium)
- DADVI (Presented at 2025 UC Berkeley biostatistics seminar)
- NRE-ABC (Presented at the BIRS Efficient Approximate Bayesian Inference Workshop 2025)
- BayesIJ (Presented at 2024 Duke statistics seminar)
- AMIP (job talk) (Presented various places 2022)
- Informal philosophy talks (from the 2022 Broderick group meetings at MIT)
- Variational methods and the EM algorithm for latent variable problems
- BLAST Day 1 (Presented at Johns Hopkins BLAST group 2021)
- BLAST Day 2 (Presented at Johns Hopkins BLAST group 2021)
- Uncertainty for the EM algorithm (Presented at the UC Berkeley Yu Group 2019)
Teaching and reading groups
Below are select websites for reading groups and courses I have (co-)designed and (co-)taught at UC Berkeley.
- STAT151A (linear models)
- STAT154 (machine learning)
- STAT298 (Statistical foundations reading group)
- Simulation based inference reading group
- Variational inference reading group
- Gaussian processes reading group