33 citations · 35 across the 5 of their papers we have counts for
5 papers
Sensitivity of MCMC-based analyses to small-data removal
Tin D. Nguyen, Ryan Giordano, Rachael Meager +1
If the conclusion of a data analysis is sensitive to dropping very few data points, that conclusion might hinge on the particular data at hand rather than representing a more broad…
Could dropping a few cells change the takeaways from differential expression?
Miriam Shiffman, Ryan Giordano, Tamara Broderick
Differential expression (DE) plays a fundamental role toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While an…
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikołaj J. Kasprzak, Ryan Giordano, Tamara Broderick
The Laplace approximation is a popular method for constructing a Gaussian approximation to the Bayesian posterior and thereby approximating the posterior mean and variance. But app…
Developing a Series of AI Challenges for the United States Department of the Air Force
Vijay Gadepally, Gregory Angelides, Andrei Barbu +39
Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have…
Edge-exchangeable graphs and sparsity
Tamara Broderick, Diana Cai
A known failing of many popular random graph models is that the Aldous-Hoover Theorem guarantees these graphs are dense with probability one; that is, the number of edges grows qua…