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Article
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
  • Haohan Wang, Carnegie Mellon University
  • Zeyi Huang, University of Wisconsin-Madison
  • Hanlin Zhang, Carnegie Mellon University
  • Yong Jae Lee, University of Wisconsin-Madison
  • Eric P. Xing, Carnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence & Petuum, Inc.
Document Type
Conference Proceeding
Abstract

Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. In this paper, we aim to offer another view of this problem in a perspective assuming the reason behind this accuracy drop is the reliance of models on the features that are not aligned well with how a data annotator considers similar across these two datasets. We refer to these features as misaligned features. We extend the conventional generalization error bound to a new one for this setup with the knowledge of how the misaligned features are associated with the label. Our analysis offers a set of techniques for this problem, and these techniques are naturally linked to many previous methods in robust machine learning literature. We also compared the empirical strength of these methods demonstrated the performance when these previous techniques are combined, with implementation available here.

Publication Date
2-28-2022
Keywords
  • robustness,
  • human-aligned,
  • domain adaptation,
  • data-oriented learning
Comments

IR conditions: non-described

Citation Information
Wang, H., Huang, Z., Zhang, H., Lee, Y.J. and, Xing, E.P., "Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features", in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, p. 2075-2084, Elndhoven, Aug 2022, Available at: https://openreview.net/forum?id=SSBzCDUiqg9