In this topic, we will investigate important theoretical machine learning problems that have high impacts on several medical applications. It includes but is not limited to optimization formulation to incorporate efficient user’s feedback to boost the performance of trained models besides available training data (active learning), investigate benefits of transfer learning strategies when dealing with scarce data issues in medical problems, or training algorithms to adapt with highly imbalanced data distribution.

Wilder, Bryan, Eric Horvitz, and Ece Kamar. “Learning to complement humans.” arXiv preprint arXiv:2005.00582 (2020).

De, Abir, et al. “Classification Under Human Assistance.” AAAI (2021).

Yao, Huaxiu, et al. “Hierarchically structured meta-learning.” International Conference on Machine Learning. PMLR, 2019.

Contact: ho_minh_duy.nguyen@dfki.de