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Martin Pawelczyk
Martin Pawelczyk
Postdoc, Harvard University
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
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 (TNNLS), 2021
4702021
Learning model-agnostic counterfactual explanations for tabular data
M Pawelczyk, K Broelemann, G Kasneci
Proceedings of The Web Conference (WWW) 2020, 3126-3132, 2020
1812020
OpenXAI: Towards a Transparent Evaluation of Model Explanations
C Agarwal, E Saxena, S Krishna, M Pawelczyk, N Johnson, I Puri, M Zitnik, ...
Proceedings of the Neural Information Processing Systems (NeurIPS) 2022 …, 2022
892022
Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms
M Pawelczyk, S Bielawski, J Heuvel, T Richter, G Kasneci
Proceedings of the Neural Information Processing Systems (NeurIPS) 2021 …, 2021
812021
Language models are realistic tabular data generators
V Borisov, K Seßler, T Leemann, M Pawelczyk, G Kasneci
11th International Conference on Learning Representations (ICLR) 2023, 2022
712022
On counterfactual explanations under predictive multiplicity
M Pawelczyk, K Broelemann, G Kasneci
Conference on Uncertainty in Artificial Intelligence (UAI), 809-818, 2020
652020
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
M Pawelczyk, C Agarwal, S Joshi, S Upadhyay, H Lakkaraju
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021
58*2021
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
M Pawelczyk, T Datta, J van-den-Heuvel, G Kasneci, H Lakkaraju
11th International Conference on Learning Representations (ICLR) 2023, 2022
41*2022
Rethinking stability for attribution-based explanations
C Agarwal, N Johnson, M Pawelczyk, S Krishna, E Saxena, M Zitnik, ...
arXiv preprint arXiv:2203.06877, 2022
292022
Linking Aid to the Sustainable Development Goals–a machine learning approach
A Pincet, S Okabe, M Pawelczyk
OECD, 2019
292019
In-context unlearning: Language models as few shot unlearners
M Pawelczyk, S Neel, H Lakkaraju
arXiv preprint arXiv:2310.07579, 2023
172023
On the Privacy Risks of Algorithmic Recourse
M Pawelczyk, H Lakkaraju, S Neel
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2022
142022
Leveraging model inherent variable importance for stable online feature selection
J Haug, M Pawelczyk, K Broelemann, G Kasneci
26th ACM SIGKDD International Conference on Knowledge Discovery & Data …, 2020
132020
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
M Pawelczyk, T Leemann, A Biega, G Kasneci
11th International Conference on Learning Representations (ICLR) 2023, 2022
102022
Model selection in local approximation gaussian processes: A markov random fields approach
H Jalali, M Pawelczyk, G Kasneci
2021 IEEE International Conference on Big Data (Big Data), 768-778, 2021
5*2021
I Prefer Not To Say: Protecting User Consent in Models with Optional Personal Data
T Leemann, M Pawelczyk, CT Eberle, G Kasneci
Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21312 …, 2024
12024
Gaussian Membership Inference Privacy
T Leemann*, M Pawelczyk*, G Kasneci
Proceedings of the Neural Information Processing Systems (NeurIPS) 2023, 2023
12023
Decomposing Counterfactual Explanations for Consequential Decision Making
M Pawelczyk, L Tiyavorabun, G Kasneci
Workshop on Socially Responsible Machine Learning (SRML), ICLR 2022, 2022
12022
Towards Non-Adversarial Algorithmic Recourse
T Leemann, M Pawelczyk, B Prenkaj, G Kasneci
arXiv preprint arXiv:2403.10330, 2024
2024
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
M Pawelczyk
Universität Tübingen, 2023
2023
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