| Deep neural networks and tabular data: A survey V Borisov, T Leemann, K Seßler, J Haug, M Pawelczyk, G Kasneci IEEE transactions on neural networks and learning systems, 2022 | 607 | 2022 |
| Language models are realistic tabular data generators V Borisov, K Seßler, T Leemann, M Pawelczyk, G Kasneci International Conference on Learning Representations (ICLR), 2023 | 118 | 2023 |
| A consistent and efficient evaluation strategy for attribution methods Y Rong*, T Leemann*, V Borisov, G Kasneci, E Kasneci International Conference on Machine Learning (ICML), 2022 | 76 | 2022 |
| Towards human-centered explainable ai: A survey of user studies for model explanations Y Rong, T Leemann, TT Nguyen, L Fiedler, P Qian, V Unhelkar, T Seidel, ... IEEE transactions on pattern analysis and machine intelligence, 2023 | 60* | 2023 |
| On the Trade-Off between Actionable Explanations and the Right to be Forgotten M Pawelczyk, T Leemann, A Biega, G Kasneci International Conference on Learning Representations (ICLR), 2023 | 15 | 2023 |
| When are post-hoc conceptual explanations identifiable? T Leemann, M Kirchhof, Y Rong, E Kasneci, G Kasneci Uncertainty in Artificial Intelligence, 1207-1218, 2023 | 9* | 2023 |
| Multi-step training for predicting roundabout traffic situations M Sackmann, T Leemann, H Bey, U Hofmann, J Thielecke 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 8 | 2021 |
| Gaussian Membership Inference Privacy T Leemann, M Pawelczyk, G Kasneci Advances in Neural Information Processing Systems (NeurIPS), 2023 | 7 | 2023 |
| Coherence evaluation of visual concepts with objects and language T Leemann, Y Rong, S Kraft, E Kasneci, G Kasneci ICLR2022 Workshop on the Elements of Reasoning: Objects, Structure and Causality, 2022 | 4 | 2022 |
| Caution to the Exemplars: On the Intriguing Effects of Example Choice on Human Trust in XAI T Leemann, Y Rong, TT Nguyen, E Kasneci, G Kasneci XAI in Action: Past, Present, and Future Applications, 2023 | 3 | 2023 |
| Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes B Prenkaj, M Villaizan-Vallelado, T Leemann, G Kasneci arXiv preprint arXiv:2308.02353, 2023 | 2 | 2023 |
| Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling T Leemann, M Sackmann, J Thielecke, U Hofmann 29th European Symposium on Artificial Neural Networks, Computational …, 2021 | 2 | 2021 |
| I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data T Leemann, M Pawelczyk, CT Eberle, G Kasneci AAAI Conference on Artificial Intelligence (AAAI-24), 2024 | 1 | 2024 |
| I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization. T Leemann, M Pawelczyk, CT Eberle, G Kasneci arXiv preprint arXiv:2210.13954, 2022 | 1 | 2022 |
| Towards Non-adversarial Algorithmic Recourse T Leemann, M Pawelczyk, B Prenkaj, G Kasneci World Conference on Explainable Artificial Intelligence, 395-419, 2024 | | 2024 |
| Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers T Leemann, A Fastowski, F Pfeiffer, G Kasneci arXiv preprint arXiv:2405.13536, 2024 | | 2024 |
| Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals B Prenkaj, M Villaizán-Vallelado, T Leemann, G Kasneci | | 2024 |
| On the Trade-Off between Actionable Explanations and the Right to be Forgotten G Kasneci, A Biega, T Leemann, M Pawelczyk arXiv, 2022 | | 2022 |
Gjergji KasneciProfessor at the Technical University of MunichVerified email at tum.de
