You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods. B Dimanov, U Bhatt, M Jamnik, A Weller IOS Press, 2020 | 106 | 2020 |
Now You See Me (CME): Concept-based Model Extraction D Kazhdan, B Dimanov, M Jamnik, P Liò, A Weller Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI), 2020 | 68 | 2020 |
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches D Kazhdan, B Dimanov, HA Terre, M Jamnik, P Liò, A Weller International Conference on Learning Representations (ICLR) Workshop on …, 2021 | 16 | 2021 |
MEME: Generating RNN Model Explanations via Model Extraction D Kazhdan, B Dimanov, M Jamnik, P Lio NeurIPS 2020 Workshop HAMLETS, 2020 | 14 | 2020 |
REM: an integrative rule extraction methodology for explainable data analysis in healthcare Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ... medRxiv, 2021.01. 25.21250459, 2021 | 11 | 2021 |
Failing conceptually: Concept-based explanations of dataset shift MA Wijaya, D Kazhdan, B Dimanov, M Jamnik arXiv preprint arXiv:2104.08952, 2021 | 7 | 2021 |
Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations B Dimanov | 4 | 2021 |
Gci: A (g) raph (c) oncept (i) nterpretation framework D Kazhdan, B Dimanov, LC Magister, P Barbiero, M Jamnik, P Lio arXiv preprint arXiv:2302.04899, 2023 | 3 | 2023 |
Step-Wise Sensitivity Analysis: Identifying Partially Distributed Representations For Interpretable Deep Learning B Dimanov, M Jamnik ICLR 2019 Debugging Machine Learning Models, 2019 | 1 | 2019 |
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations S Cardozo, GI Montero, D Kazhdan, B Dimanov, M Wijaya, M Jamnik, ... arXiv preprint arXiv:2211.07650, 2022 | | 2022 |
Method for inspecting a neural network BT Dimanov, M Jamnik US Patent 11,449,578, 2022 | | 2022 |