Conference Program Home
  My Program

All Times EDT

Legend:
* = applied session       ! = JSM meeting theme

Activity Details

103 Mon, 8/8/2022, 8:30 AM - 10:20 AM CC-101
Uncertainty Quantification for Machine Learning — Topic Contributed Papers
Section on Physical and Engineering Sciences, Section on Statistics in Defense and National Security, Uncertainty Quantification in Complex Systems Interest Group
Organizer(s): Michael Grosskopf, Los Alamos National Laboratory; Natalie Klein, Los Alamos National Laboratory
Chair(s): Natalie Klein, Los Alamos National Laboratory
8:35 AM Myths and Reality in Bayesian Deep Learning
Andrew Gordon Wilson, New York University
8:55 AM Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations
Erin Acquesta, Sandia National Laboratories; Teresa Portone, Sandia National Laboratories; Christopher Rackauckas, Massachusetts Institue of Technology; Raj Dandekar, Massachusetts Institute of Technology
9:15 AM Generative Modeling Methods in Uncertainty Quantification and Bayesian Inference
Youssef Marzouk, Massachusetts Institute of Technology
9:35 AM Conformal Prediction and Calibration Under Distribution Drift
Aaditya Ramdas, Carnegie Mellon University; Aleksandr Podkopaev, Carnegie Mellon University
9:55 AM A Phase Transition for Finding Needles in Nonlinear Haystacks with LASSO Artificial Neural Networks
Nicolas Hengartner, Center for nonlienar studies, Los Alamos National Laboratory; Sylvain Sardy, Dept. of Mathematics, University of Geneva; Nikolai Bobenko, Universoty of Geneva; Yen Ting Lin , Infformation Sciences group, Los Alamos National Laboratory; Xiaoyu Ma, Shandong University and University of Geneva
10:15 AM Floor Discussion