Controlled Abstention Neural Networks for Identifying Skillful Predictions for Regression Problems

Elizabeth A. Barnes, Randal J. Barnes

Research output: Contribution to journalArticlepeer-review

Abstract

The earth system is exceedingly complex and often chaotic in nature, making prediction incredibly challenging: we cannot expect to make perfect predictions all of the time. Instead, we look for specific states of the system that lead to more predictable behavior than others, often termed “forecasts of opportunity.” When these opportunities are not present, scientists need prediction systems that are capable of saying “I don't know.” We introduce a novel loss function, termed “abstention loss,” that allows neural networks to identify forecasts of opportunity for regression problems. The abstention loss works by incorporating uncertainty in the network's prediction to identify the more confident samples and abstain (say “I don't know”) on the less confident samples. The abstention loss is designed to determine the optimal abstention fraction, or abstain on a user-defined fraction using a standard adaptive controller. Unlike many methods for attaching uncertainty to neural network predictions post-training, the abstention loss is applied during training to preferentially learn from the more confident samples. The abstention loss is built upon nonlinear heteroscedastic regression, a standard computer science method. While nonlinear heteroscedastic regression is a simple yet powerful tool for incorporating uncertainty in regression problems, we demonstrate that the abstention loss outperforms it for the synthetic climate use cases explored here. The implementation of the proposed abstention loss is straightforward in most network architectures designed for regression, as it only requires modification of the output layer and loss function.

Original languageEnglish (US)
Article numbere2021MS002575
JournalJournal of Advances in Modeling Earth Systems
Volume13
Issue number12
DOIs
StatePublished - Dec 2021

Bibliographical note

Funding Information:
The authors wish to thank the editor, associate editor, and two anonymous reviewers for helping us improve the paper. This work was funded in part by the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) under NSF grant ICER‐2019758.

Publisher Copyright:
© 2021 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.

Keywords

  • forecasts of opportunity
  • neural networks
  • prediction
  • regression
  • uncertainty

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