Damien Dablain

According to our database, Damien Dablain authored at least 10 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Understanding CNN fragility when learning with imbalanced data.

Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla

Mach. Learn., July, 2024

Understanding imbalanced data: XAI & interpretable ML framework.

Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla

Mach. Learn., June, 2024

The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data.

Damien A. Dablain, Nitesh V. Chawla

CoRR, 2024

Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data.

Damien A. Dablain, Nitesh V. Chawla

Proceedings of the 11th IEEE International Conference on Data Science and Advanced Analytics, 2024

2023

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

IEEE Trans. Neural Networks Learn. Syst., September, 2023

Towards Understanding How Data Augmentation Works with Imbalanced Data.

Damien A. Dablain, Nitesh V. Chawla

CoRR, 2023

Efficient Augmentation for Imbalanced Deep Learning.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

Interpretable ML for Imbalanced Data.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, 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

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning.

Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

CoRR, 2022