Martin Pawelczyk

According to our database, Martin Pawelczyk authored at least 32 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?

Franka Bause, Jonas Niederle, Martin Pawelczyk, Rebekka Burkholz

CoRR, May, 2026

Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks.

Samira Abedini, Sina Mavali, Lea Schönherr, Martin Pawelczyk, Rebekka Burkholz

CoRR, March, 2026

Easy Data Unlearning Bench.

Roy Rinberg, Pol Puigdemont, Martin Pawelczyk, Volkan Cevher

CoRR, February, 2026

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models.

Lillian Sun, Martin Pawelczyk, Zhenting Qi, Aounon Kumar, Himabindu Lakkaraju

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

2025

Train Once, Answer All: Many Pretraining Experiments for the Cost of One.

Sebastian Bordt, Martin Pawelczyk

CoRR, September, 2025

Efficiently Verifiable Proofs of Data Attribution.

Ari Karchmer, Seth Neel, Martin Pawelczyk

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Machine Unlearning Fails to Remove Data Poisoning Attacks.

Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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., June, 2024

On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

Martin Pawelczyk

PhD thesis, 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

Towards Non-adversarial Algorithmic Recourse.

Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj, Gjergji Kasneci

Proceedings of the Explainable Artificial Intelligence, 2024

In-Context Unlearning: Language Models as Few-Shot Unlearners.

Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju

Proceedings of the Forty-first International Conference on Machine Learning, 2024

I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data.

Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Gaussian Membership Inference Privacy.

Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Trade-Off between Actionable Explanations and the Right to be Forgotten.

Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Language Models are Realistic Tabular Data Generators.

Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci

Proceedings of the Eleventh International Conference on Learning Representations, 2023

On the Privacy Risks of Algorithmic Recourse.

Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Decomposing Counterfactual Explanations for Consequential Decision Making.

Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci

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

Rethinking Stability for Attribution-based Explanations.

Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju

CoRR, 2022

Algorithmic Recourse in the Face of Noisy Human Responses.

Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.

Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

On the Connections between Counterfactual Explanations and Adversarial Examples.

Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju

CoRR, 2021

Gaussian Experts Selection using Graphical Models.

Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci

CoRR, 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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach.

Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Learning Model-Agnostic Counterfactual Explanations for Tabular Data.

Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

On Counterfactual Explanations under Predictive Multiplicity.

Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Leveraging Model Inherent Variable Importance for Stable Online Feature Selection.

Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Towards User Empowerment.

Martin Pawelczyk, Johannes Haug, Klaus Broelemann, Gjergji Kasneci

CoRR, 2019