Publications

References

Berlinghieri, R., B. Trippe, D. Burt, R. Giordano, K. Srinivasan, T. Özgökmen, J. Xia, and T. Broderick. 2023. “Gaussian Processes at the Helm(holtz): A More Fluid Model for Ocean Currents.” In Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research. PMLR.

Giordano, R., and T. Broderick. 2025. “The Bayesian Infinitesimal Jackknife for Variance.” Journal of the American Statistical Association.

Giordano, R., T. Broderick, and M. I. Jordan. 2015. “Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes.” In Advances in Neural Information Processing Systems, 1441–49.

———. 2018. “Covariances, Robustness, and Variational Bayes.” Journal of Machine Learning Research 19 (51): 1–49. http://jmlr.org/papers/v19/17-670.html.

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.

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., 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.

———. 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.

Giordano, R., W. Stephenson, R. Liu, M. I. Jordan, and T. Broderick. 2019. “A Swiss Army Infinitesimal Jackknife.” In The 22nd International Conference on Artificial Intelligence and Statistics, 1139–47.

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.

Regier, J., K. Pamnany, K. Fischer, A. Noack, M. Lam, J. Revels, S. Howard, R. Giordano, D. Schlegel, and J. McAuliffe. 2018. “Cataloging the Visible Universe Through Bayesian Inference at Petascale.” In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 44–53. IEEE.

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.