Martin Pawelczyk

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 34

Top Topics

Top Venues

Publications

Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci
Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Networks Learn. Syst. 35 (6) (2024)
Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj, Gjergji Kasneci
Towards Non-Adversarial Algorithmic Recourse. CoRR (2024)
Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci
I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data. AAAI (2024)
Catherine Huang, Martin Pawelczyk, Himabindu Lakkaraju
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference. CoRR (2024)
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
Machine Unlearning Fails to Remove Data Poisoning Attacks. CoRR (2024)
Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci
On the Trade-Off between Actionable Explanations and the Right to be Forgotten. ICLR (2023)
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
Language Models are Realistic Tabular Data Generators. ICLR (2023)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. AISTATS (2023)
Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
Gaussian Membership Inference Privacy. CoRR (2023)
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
In-Context Unlearning: Language Models as Few Shot Unlearners. CoRR (2023)
Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
Gaussian Membership Inference Privacy. NeurIPS (2023)
Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. ICLR (2023)
Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
OpenXAI: Towards a Transparent Evaluation of Model Explanations. CoRR (2022)
Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju
Algorithmic Recourse in the Face of Noisy Human Responses. CoRR (2022)
Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci
On the Trade-Off between Actionable Explanations and the Right to be Forgotten. CoRR (2022)
Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci
Decomposing Counterfactual Explanations for Consequential Decision Making. CoRR (2022)
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci
Language Models are Realistic Tabular Data Generators. CoRR (2022)
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
On the Privacy Risks of Algorithmic Recourse. CoRR (2022)
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
OpenXAI: Towards a Transparent Evaluation of Model Explanations. NeurIPS (2022)
Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, Himabindu Lakkaraju
Rethinking Stability for Attribution-based Explanations. CoRR (2022)
Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS (2022)
Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci
I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization. CoRR (2022)
Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju
On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR (2021)
Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci
Gaussian Experts Selection using Graphical Models. CoRR (2021)
Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci
Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach. IEEE BigData (2021)
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci
Deep Neural Networks and Tabular Data: A Survey. CoRR (2021)
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, Gjergji Kasneci
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. NeurIPS Datasets and Benchmarks (2021)
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, Gjergji Kasneci
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. CoRR (2021)
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Learning Model-Agnostic Counterfactual Explanations for Tabular Data. WWW (2020)
Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. CoRR (2020)
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
On Counterfactual Explanations under Predictive Multiplicity. UAI (2020)
Johannes Haug, Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection. KDD (2020)
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
On Counterfactual Explanations under Predictive Multiplicity. CoRR (2020)
Martin Pawelczyk, Johannes Haug, Klaus Broelemann, Gjergji Kasneci
Towards User Empowerment. CoRR (2019)