Martin Pawelczyk

According to our database1, Martin Pawelczyk authored at least 22 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
In-Context Unlearning: Language Models as Few Shot Unlearners.
CoRR, 2023

Gaussian Membership Inference Privacy.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Language Models are Realistic Tabular Data Generators.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

On the Privacy Risks of Algorithmic Recourse.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Decomposing Counterfactual Explanations for Consequential Decision Making.
CoRR, 2022

I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization.
CoRR, 2022

Rethinking Stability for Attribution-based Explanations.
CoRR, 2022

Algorithmic Recourse in the Face of Noisy Human Responses.
CoRR, 2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Deep Neural Networks and Tabular Data: A Survey.
CoRR, 2021

On the Connections between Counterfactual Explanations and Adversarial Examples.
CoRR, 2021

Gaussian Experts Selection using Graphical Models.
CoRR, 2021

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms.
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.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Learning Model-Agnostic Counterfactual Explanations for Tabular Data.
Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

On Counterfactual Explanations under Predictive Multiplicity.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Leveraging Model Inherent Variable Importance for Stable Online Feature Selection.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019
Towards User Empowerment.
CoRR, 2019


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