147 citations · 307 across the 9 of their papers we have counts for
15 papers
Post-hoc loss-calibration for Bayesian neural networks
Meet P. Vadera, Soumya Ghosh, Kenney Ng +1
Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available. Modern Bayesian models, however,…
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy +4
In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is t…
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Jiri Navratil, Benjamin Elder, Matthew Arnold +2
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Siddharth Biswal, Soumya Ghosh, Jon Duke +3
Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the q…
Model Fusion with Kullback--Leibler Divergence
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh +1
We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual…
Approximate Cross-Validation for Structured Models
Soumya Ghosh, William T. Stephenson, Tin D. Nguyen +2
Many modern data analyses benefit from explicitly modeling dependence structure in data -- such as measurements across time or space, ordered words in a sentence, or genes in a gen…