Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning. - Damien Dablain; Bartosz Krawczyk; Nitesh V. Chawla (CoRR, 2022)

Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology. - Damien Dablain; Lilian Huang; Brandon Sepulvado (CoRR, 2022)

Interpretable ML for Imbalanced Data. - Damien A. Dablain; Colin Bellinger; Bartosz Krawczyk; David W. Aha; Nitesh V. Chawla (CoRR, 2022)

Efficient Augmentation for Imbalanced Deep Learning. - Damien A. Dablain; Colin Bellinger; Bartosz Krawczyk; Nitesh V. Chawla (ICDE, 2023)

Towards Understanding How Data Augmentation Works with Imbalanced Data. - Damien A. Dablain; Nitesh V. Chawla (CoRR, 2023)

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. - Damien Dablain; Bartosz Krawczyk; Nitesh V. Chawla (IEEE Trans. Neural Networks Learn. Syst., 2023)

Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data. - Damien A. Dablain; Nitesh V. Chawla (DSAA, 2024)

The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data. - Damien A. Dablain; Nitesh V. Chawla (ICPRAI (1), 2024)

Understanding imbalanced data: XAI & interpretable ML framework. - Damien Dablain; Colin Bellinger; Bartosz Krawczyk; David W. Aha; Nitesh V. Chawla (Mach. Learn., 2024)

Understanding CNN fragility when learning with imbalanced data. - Damien Dablain; Kristen N. Jacobson; Colin Bellinger; Mark Roberts; Nitesh V. Chawla (Mach. Learn., 2024)