I am a third-year Computer Sciences PhD student at UW-Madison, where I am co-advised by Aws Albarghouthi and Loris D’Antoni.

My work is motivated by the mismatch between machine learning’s influence (and presumed authority), and the fact that many machine learning outcomes are arbitrary, i.e., not robust to small changes in the training data, training procedure, or test sample.

To that end, I have worked on certifying the robustness of machine learning models to small changes to the training data. I am also interesting in how we can adjust the training procedure for machine learning models so that the model explanations will be more robust.



On Minimizing the Impact of Dataset Shifts on Actionable Explanations
Anna P. Meyer (+), Dan Ley (+), Suraj Srinivas, and Himabindu Lakkaraju
UAI 2023 (Oral Presentation)
[pdf] [code]
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
Anna P. Meyer, Aws Albarghouthi, and Loris D’Antoni
FAccT 2023
[pdf] [video] [code]
Certifying Robustness to Programmable Data Bias in Decision Trees
Anna P. Meyer, Aws Albarghouthi, and Loris D’Antoni
NeurIPS 2021
[pdf] [slides] [video] [code]

(+) Equal contribution