| DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data D Dablain, B Krawczyk, NV Chawla IEEE Transactions on Neural Networks and Learning Systems, 2022 | 194 | 2022 |
| Understanding CNN fragility when learning with imbalanced data D Dablain, KN Jacobson, C Bellinger, M Roberts, NV Chawla Machine Learning, 1-26, 2023 | 13 | 2023 |
| Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning D Dablain, B Krawczyk, N Chawla arXiv preprint arXiv:2207.06084, 2022 | 8 | 2022 |
| Efficient augmentation for imbalanced deep learning DA Dablain, C Bellinger, B Krawczyk, NV Chawla 2023 IEEE 39th International Conference on Data Engineering (ICDE), 1433-1446, 2023 | 5 | 2023 |
| Towards Understanding How Data Augmentation Works with Imbalanced Data DA Dablain, NV Chawla arXiv preprint arXiv:2304.05895, 2023 | 2 | 2023 |
| Interpretable ML for Imbalanced Data DA Dablain, C Bellinger, B Krawczyk, DW Aha, NV Chawla arXiv preprint arXiv:2212.07743, 2022 | 2 | 2022 |
| Generative AI Design and Exploration of Nucleoside Analogs D Dablain, G Siwo, N Chawla | 2 | 2021 |
| Understanding imbalanced data: XAI & interpretable ML framework D Dablain, C Bellinger, B Krawczyk, DW Aha, N Chawla Machine Learning, 1-19, 2024 | 1 | 2024 |
| Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning D Dablain, B Krawczyk, N Chawla Discover Data 2 (1), 4, 2024 | | 2024 |
| Linear Data Augmentation to Improve Generalization for Imbalanced Learning DA Dablain University Of Notre Dame, 2023 | | 2023 |
| Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology D Dablain, L Huang, B Sepulvado arXiv preprint arXiv:2207.06360, 2022 | | 2022 |
| Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology | | |
