3 citations · 12 across the 17 of their papers we have counts for
19 papers
Model-free generalized fiducial inference
Jonathan P Williams
Conformal prediction (CP) was developed to provide finite-sample probabilistic prediction guarantees. While CP algorithms are a relatively general-purpose approach to uncertainty q…
Introduction to Generalized Fiducial Inference
Alexander C. Murph, Jan Hannig, Jonathan P. Williams
Fiducial inference was introduced in the first half of the 20th century by Fisher (1935) as a means to get a posterior-like distribution for a parameter without having to arbitrari…
Valid Inference for Machine Learning Model Parameters
Neil Dey, Jonathan P. Williams
The parameters of a machine learning model are typically learned by minimizing a loss function on a set of training data. However, this can come with the risk of overtraining; in o…
Word Embeddings as Statistical Estimators
Neil Dey, Matthew Singer, Jonathan P. Williams +1
Word embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets,…
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Jimmy Hickey, Jonathan P. Williams, Emily C. Hector
Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another populati…
Beyond time-homogeneity for continuous-time multistate Markov models
Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen +2
Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov…