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Tobias Leemann
Tobias Leemann
在 uni-tuebingen.de 的电子邮件经过验证 - 首页
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引用次数
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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
5682022
Language models are realistic tabular data generators
V Borisov, K Seßler, T Leemann, M Pawelczyk, G Kasneci
International Conference on Learning Representations (ICLR), 2023
1032023
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
712022
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
52*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
122023
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
82021
When are post-hoc conceptual explanations identifiable?
T Leemann, M Kirchhof, Y Rong, E Kasneci, G Kasneci
Uncertainty in Artificial Intelligence, 1207-1218, 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
42022
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
32023
Gaussian Membership Inference Privacy
T Leemann, M Pawelczyk, G Kasneci
Advances in Neural Information Processing Systems (NeurIPS), 2023
32023
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
22021
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
12024
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
12023
I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization.
T Leemann, M Pawelczyk, CT Eberle, G Kasneci
CoRR, 2022
12022
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
Towards Non-Adversarial Algorithmic Recourse
T Leemann, M Pawelczyk, B Prenkaj, G Kasneci
arXiv preprint arXiv:2403.10330, 2024
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
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