Towards User Empowerment. - Martin Pawelczyk; Johannes Haug; Klaus Broelemann; Gjergji Kasneci (CoRR, 2019)

Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. - Johannes Haug; Martin Pawelczyk; Klaus Broelemann; Gjergji Kasneci (KDD, 2020)

On Counterfactual Explanations under Predictive Multiplicity. - Martin Pawelczyk; Klaus Broelemann; Gjergji Kasneci (UAI, 2020)

Learning Model-Agnostic Counterfactual Explanations for Tabular Data. - Martin Pawelczyk; Klaus Broelemann; Gjergji Kasneci (WWW, 2020)

Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach. - Hamed Jalali 001; Martin Pawelczyk; Gjergji Kasneci (IEEE BigData, 2021)

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. - Martin Pawelczyk; Sascha Bielawski; Johannes van den Heuvel; Tobias Richter; Gjergji Kasneci (NeurIPS Datasets and Benchmarks, 2021)

Gaussian Experts Selection using Graphical Models. - Hamed Jalali 001; Martin Pawelczyk; Gjergji Kasneci (CoRR, 2021)

On the Connections between Counterfactual Explanations and Adversarial Examples. - Martin Pawelczyk; Shalmali Joshi; Chirag Agarwal; Sohini Upadhyay; Himabindu Lakkaraju (CoRR, 2021)

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. - Martin Pawelczyk; Chirag Agarwal; Shalmali Joshi; Sohini Upadhyay; Himabindu Lakkaraju (AISTATS, 2022)

OpenXAI: Towards a Transparent Evaluation of Model Explanations. - Chirag Agarwal; Satyapriya Krishna; Eshika Saxena; Martin Pawelczyk; Nari Johnson; Isha Puri; Marinka Zitnik; Himabindu Lakkaraju (NeurIPS, 2022)

Algorithmic Recourse in the Face of Noisy Human Responses. - Martin Pawelczyk; Teresa Datta; Johannes van den Heuvel; Gjergji Kasneci; Himabindu Lakkaraju (CoRR, 2022)

Rethinking Stability for Attribution-based Explanations. - Chirag Agarwal; Nari Johnson; Martin Pawelczyk; Satyapriya Krishna; Eshika Saxena; Marinka Zitnik; Himabindu Lakkaraju (CoRR, 2022)

I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization. - Tobias Leemann; Martin Pawelczyk; Christian Thomas Eberle; Gjergji Kasneci (CoRR, 2022)

Decomposing Counterfactual Explanations for Consequential Decision Making. - Martin Pawelczyk; Lea Tiyavorabun; Gjergji Kasneci (CoRR, 2022)

On the Privacy Risks of Algorithmic Recourse. - Martin Pawelczyk; Himabindu Lakkaraju; Seth Neel (AISTATS, 2023)

Language Models are Realistic Tabular Data Generators. - Vadim Borisov; Kathrin Seßler; Tobias Leemann; Martin Pawelczyk; Gjergji Kasneci (ICLR, 2023)

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 (ICLR, 2023)

On the Trade-Off between Actionable Explanations and the Right to be Forgotten. - Martin Pawelczyk; Tobias Leemann; Asia Biega; Gjergji Kasneci (ICLR, 2023)

Gaussian Membership Inference Privacy. - Tobias Leemann; Martin Pawelczyk; Gjergji Kasneci (NeurIPS, 2023)

I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data. - Tobias Leemann; Martin Pawelczyk; Christian Thomas Eberle; Gjergji Kasneci (AAAI, 2024)

In-Context Unlearning: Language Models as Few-Shot Unlearners. - Martin Pawelczyk; Seth Neel; Himabindu Lakkaraju (ICML, 2024)

Towards Non-adversarial Algorithmic Recourse. - Tobias Leemann; Martin Pawelczyk; Bardh Prenkaj; Gjergji Kasneci (xAI (3), 2024)

Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. - Catherine Huang; Martin Pawelczyk; Himabindu Lakkaraju (CoRR, 2024)

On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse - Martin Pawelczyk (2024)

Deep Neural Networks and Tabular Data: A Survey. - Vadim Borisov; Tobias Leemann; Kathrin Seßler; Johannes Haug; Martin Pawelczyk; Gjergji Kasneci (IEEE Trans. Neural Networks Learn. Syst., 2024)

Machine Unlearning Fails to Remove Data Poisoning Attacks. - Martin Pawelczyk; Jimmy Z. Di; Yiwei Lu 001; Gautam Kamath 001; Ayush Sekhari; Seth Neel (ICLR, 2025)

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models. - Martin Pawelczyk; Lillian Sun; Zhenting Qi; Aounon Kumar; Himabindu Lakkaraju (CoRR, 2025)