168 citations · 1.5k across the 73 of their papers we have counts for
74 papers
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…
An imprecise-probabilistic characterization of frequentist statistical inference
Ryan Martin
Between the two dominant schools of thought in statistics, namely, Bayesian and classical/frequentist, a main difference is that the former is grounded in the mathematically rigoro…
Decision-making with possibilistic inferential models
Ryan Martin, Shih-Ni Prim, Jonathan Williams
Inferential models (IMs) are data-dependent, imprecise-probabilistic structures designed to quantify uncertainty about unknowns. As the name suggests, the focus has been on uncerta…
Valid inferential models for prediction in supervised learning problems
Leonardo Cella, Ryan Martin
Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guaran…
Direct and approximately valid probabilistic inference on a class of statistical functionals
Leonardo Cella, Ryan Martin
Existing frameworks for probabilistic inference assume the quantity of interest is the parameter of a posited statistical model. In machine learning applications, however, often th…
Revisiting consistency of a recursive estimator of mixing distributions
Vaidehi Dixit, Ryan Martin
Estimation of the mixing distribution under a general mixture model is a very difficult problem, especially when the mixing distribution is assumed to have a density. Predictive re…