Fine-grained neural network explanation by identifying input features with predictive information Y Zhang*, A Khakzar*, Y Li, A Farshad, ST Kim, N Navab Advances in Neural Information Processing Systems 34, 20040-20051, 2021 | 26 | 2021 |
Explaining covid-19 and thoracic pathology model predictions by identifying informative input features A Khakzar, Y Zhang, W Mansour, Y Cai, Y Li, Y Zhang, ST Kim, N Navab Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 16 | 2021 |
Deep learning‐based classification of dermatological lesions given a limited amount of labelled data S Krammer, Y Li, N Jakob, AS Boehm, H Wolff, P Tang, T Lasser, ... Journal of the European Academy of Dermatology and Venereology 36 (12), 2516 …, 2022 | 4 | 2022 |
Probabilistic self-supervised learning via scoring rules minimization A Vahidi, S Schoßer, L Wimmer, Y Li, B Bischl, E Hüllermeier, M Rezaei arXiv preprint arXiv:2309.02048, 2023 | 1 | 2023 |
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models Y Li*, A Khakzar*, Y Zhang, M Sanisoglu, ST Kim, M Rezaei, B Bischl, ... ICML 2022 Workshop on Interpretable Machine Learning in Healthcare, 2022 | 1* | 2022 |
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments Y Zhang*, Y Li*, H Brown, M Rezaei, B Bischl, P Torr, A Khakzar, ... arXiv preprint arXiv:2310.06514, 2023 | | 2023 |
A Dual-Perspective Approach to Evaluating Feature Attribution Methods Y Li*, Y Zhang*, K Kawaguchi, A Khakzar, B Bischl, M Rezaei arXiv preprint arXiv:2308.08949, 2023 | | 2023 |
Logistic Regression with Robust Bootstrapping Y Li, M Fauß, AM Zoubir 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | | 2019 |