2 citations · 2 across the 10 of their papers we have counts for
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Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?
Anna Richter, Julia Stoyanovich, Sebastian Schelter
Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible t…
Explanation Multiplicity in SHAP: Characterization and Assessment
Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt +2
SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and…
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
Shlomi Hod, Lucas Rosenblatt, Julia Stoyanovich
Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretrai…