Title |
|---|
 Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methodsKnowledge and Information Systems (KAIS), 2024 |
 Understanding Disparities in Post Hoc Machine Learning ExplanationConference on Fairness, Accountability and Transparency (FAccT), 2024 |
 A General Recipe for Automated Machine Learning in PracticeIbero-American Conference on AI (IBERAMIA), 2023 |
 AutoML in The Wild: Obstacles, Workarounds, and ExpectationsInternational Conference on Human Factors in Computing Systems (CHI), 2023 |
 The Road to Explainability is Paved with Bias: Measuring the Fairness of
ExplanationsConference on Fairness, Accountability and Transparency (FAccT), 2022 |
