Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)

Authors

  • Josué Martínez-Martínez University of Connecticut MIT Lincoln Laboratory
  • Olivia Brown MIT Lincoln Laboratory
  • Rajmonda Caceres MIT Lincoln Laboratory

DOI:

https://doi.org/10.1609/aaai.v38i21.30481

Keywords:

Robustness, Distribution Shift, Natural Variation, Deep Learning, AI Safety

Abstract

This research focuses on improving the robustness of machine learning systems to natural variations and distribution shifts. A design trade space is presented, and various methods are compared, including adversarial training, data augmentation techniques, and novel approaches inspired by model-based robust optimization formulations.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Martínez-Martínez, J., Brown, O., & Caceres, R. (2024). Towards Robustness to Natural Variations and Distribution Shift (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23579–23581. https://doi.org/10.1609/aaai.v38i21.30481

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program