AAAI 2024technical0 citations
Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)
Josué Martínez-Martínez, Olivia Brown, Rajmonda Caceres
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.
BibTeX
@article{Martínez-Martínez_Brown_Caceres_2024, title={Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30481}, DOI={10.1609/aaai.v38i21.30481}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Martínez-Martínez, Josué and Brown, Olivia and Caceres, Rajmonda}, year={2024}, month={Mar.}, pages={23579-23581} }