JSM2024
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Invited Paper Session

A Statistical Perspective on Uncertainty Quantification for Deep Learning

Tue, Aug 6, 10:30 AM - 12:20 PM
Daniel RiesOrganizerJason AdamsChair
Section on Statistics in Defense and National Security

5 Presentations

Advancing Approximate Bayesian Inference and Model Selection for Neural Networks
10:35 AM - 10:55 AM
Natalie Klein (Los Alamos National Laboratory)
Deep Kernel Learning for Multi-model Ensemble Analysis
10:55 AM - 11:15 AM
Trevor Harris
Real World UQ Quality Assessment via Surrogate Oracle Modeling
11:15 AM - 11:35 AM
Joshua Michalenko (Sandia National Laboratories)
Regression Recalibration by CRPS Minimization
11:35 AM - 11:55 AM
Feng Liang (University of Illinois at Urbana-Champaign)
Uncertainty Quantification and Calibrating Confidence Scores in Language Models
11:55 AM - 12:15 PM
Karl Pazdernik (Pacific Northwest National Laboratory)
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