| 2026 | ACL | Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models. | Lillian Sun, Martin Pawelczyk, Zhenting Qi, Aounon Kumar, Himabindu Lakkaraju |
| 2025 | ICLR | Machine Unlearning Fails to Remove Data Poisoning Attacks. | Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel |
| 2024 | AAAI | I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data. | Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci |
| 2024 | ICML | In-Context Unlearning: Language Models as Few-Shot Unlearners. | Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju |
| 2023 | AISTATS | On the Privacy Risks of Algorithmic Recourse. | Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel |
| 2023 | ICLR | Language Models are Realistic Tabular Data Generators. | Vadim Borisov, Kathrin Seler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci |
| 2023 | ICLR | Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. | Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju |
| 2023 | ICLR | On the Trade-Off between Actionable Explanations and the Right to be Forgotten. | Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci |
| 2022 | AISTATS | Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. | Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju |
| 2020 | KDD | Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. | Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci |
| 2020 | WWW | Learning Model-Agnostic Counterfactual Explanations for Tabular Data. | Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci |
| 2020 | UAI | On Counterfactual Explanations under Predictive Multiplicity. | Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci |