Publications

Quantifying Uncertainty to Improve Decision Making in Machine Learning

Stracuzzi, David J.; Darling, Michael C.; Peterson, Matthew G.; Chen, Maximillian G.

Data-driven modeling, including machine learning methods, continue to play an increas- ing role in society. Data-driven methods impact decision making for applications ranging from everyday determinations about which news people see and control of self-driving cars to high-consequence national security situations related to cyber security and analysis of nuclear weapons reliability. Although modern machine learning methods have made great strides in model induction and show excellent performance in a broad variety of complex domains, uncertainty remains an inherent aspect of any data-driven model. In this report, we provide an update to the preliminary results on uncertainty quantifi- cation for machine learning presented in SAND2017-6776. Specifically, we improve upon the general problem definition and expand upon the experiments conducted for the earlier re- port. Most importantly, we summarize key lessons learned about how and when uncertainty quantification can inform decision making and provide valuable insights into the quality of learned models and potential improvements to them. Acknowledgements The authors thank Kristina Czuchlewski, John Feddema, Todd Jones, Chris Young, Rudy Garcia, Rich Field, Ann Speed, Randy Brost, Stephen Dauphin, and countless others for providing helpful discussion and comments throughout the life of this project. This work was funded by the Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program.