Wind Prediction under Random Data Corruption (Student Abstract)


  • Conner Flansburg University of Oklahoma
  • Dimitrios I. Diochnos University of Oklahoma



Adversarial Machine Learning, Training-Time Attacks, Random Data Corruption, Certified Robustness, Atmospheric Science


We study the robustness of ridge regression, lasso regression, and of a neural network, when the training set has been randomly corrupted and in response to this corruption the training-size is reduced in order to remove the corrupted data. While the neural network appears to be the most robust method among these three, nevertheless lasso regression appears to be the method of choice since it suffers less loss both when the full information is available to the learner, as well as when a significant amount of the original training set has been rendered useless because of random data corruption.




How to Cite

Flansburg, C., & Diochnos, D. I. (2022). Wind Prediction under Random Data Corruption (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12945-12946.