Jonathan P. Williams

According to our database, Jonathan P. Williams authored at least 14 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

A Bayesian shrinkage estimator for transfer learning.

Mohamed A. Abba, Jonathan P. Williams, Brian J. Reich

J. Multivar. Anal., 2026

Generalized fiducial inference on differentiable manifolds.

Alexander C. Murph, Jonathan P. Williams, Jan Hannig

Int. J. Approx. Reason., 2026

2025

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction.

Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams

Int. J. Data Sci. Anal., December, 2025

Beyond Time-Homogeneity for Continuous-Time Multistate Markov Models.

Emmett B. Kendall, Jonathan P. Williams, Gudmund Horn Hermansen, Frédéric Y. Bois, Vo Hong Thanh

J. Comput. Graph. Stat., 2025

Asymptotic efficiency of inferential models and a possibilistic Bernstein-von Mises theorem.

Ryan Martin, Jonathan P. Williams

Int. J. Approx. Reason., 2025

2024

Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST).

Jimmy Hickey, Jonathan P. Williams, Emily C. Hector

J. Mach. Learn. Res., 2024

Clustered Conformal Prediction for the Housing Market.

Anders Hjort, Jonathan P. Williams, Johan Pensar

Proceedings of the 13th Symposium on Conformal and Probabilistic Prediction with Applications, 2024

Decision Theory via Model-Free Generalized Fiducial Inference.

Jonathan P. Williams, Yang Liu

Proceedings of the Belief Functions: Theory and Applications, 2024

Large-Sample Theory for Inferential Models: A Possibilistic Bernstein-von Mises Theorem.

Ryan Martin, Jonathan P. Williams

Proceedings of the Belief Functions: Theory and Applications, 2024

2023

Uncertainty quantification in automated valuation models with locally weighted conformal prediction.

Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams

CoRR, 2023

Model-free generalized fiducial inference.

Jonathan P. Williams

CoRR, 2023

Valid Inference for Machine Learning Model Parameters.

Neil Dey, Jonathan P. Williams

CoRR, 2023

2021

Conformal prediction for text infilling and part-of-speech prediction.

Neil Dey, Jing Ding, Jack Ferrell, Carolina Kapper, Maxwell Lovig, Emiliano Planchon, Jonathan P. Williams

CoRR, 2021

2015

Covariance Selection in the Linear Mixed Effect Mode.

Jonathan P. Williams, Ying Lu

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015