58 citations · 172 across the 16 of their papers we have counts for
8 papers · 1 filter
Direct Gibbs posterior inference on risk minimizers: construction, concentration, and calibration
Ryan Martin, Nicholas Syring
Real-world problems, often couched as machine learning applications, involve quantities of interest that have real-world meaning, independent of any statistical model. To avoid pot…
Calibrating generalized predictive distributions
Pei-Shien Wu, Ryan Martin
In prediction problems, it is common to model the data-generating process and then use a model-based procedure, such as a Bayesian predictive distribution, to quantify uncertainty…
Variational approximations of empirical Bayes posteriors in high-dimensional linear models
Yue Yang, Ryan Martin
In high-dimensions, the prior tails can have a significant effect on both posterior computation and asymptotic concentration rates. To achieve optimal rates while keeping the poste…
Model-free posterior inference on the area under the receiver operating characteristic curve
Zhe Wang, Ryan Martin
The area under the receiver operating characteristic curve (AUC) serves as a summary of a binary classifier's performance. Methods for estimating the AUC have been developed under…
Permutation-based uncertainty quantification about a mixing distribution
Vaidehi Dixit, Ryan Martin
Nonparametric estimation of a mixing distribution based on data coming from a mixture model is a challenging problem. Beyond estimation, there is interest in uncertainty quantifica…
Optimal inferential models for a Poisson mean
Ryan Martin, Duncan Ermini Leaf, Chuanhai Liu
Statistical inference on the mean of a Poisson distribution is a fundamentally important problem with modern applications in, e.g., particle physics. The discreteness of the Poisso…