Martin Pawelczyk
12 papers · 2020–2026 · 7 venues · across top CS/AI venues
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Research topics
Keywords
counterfactual explanation
(3)
algorithmic recourse
(3)
large language model
(2)
machine unlearning
(2)
membership inference
(2)
data poisoning
(1)
few-shot learning
(1)
stochastic gradient descent
(1)
explainable ai
(1)
autoregressive generation
(1)
adversarial robustness
(1)
feature attribution
(1)
deep learning
(1)
theoretical analysis
(1)
variational autoencoder
(1)
data augmentation
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data privacy
(1)
model training
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synthetic data
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model interpretability
(1)
Papers
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models
ACL 2026
Machine Unlearning Fails to Remove Data Poisoning Attacks
ICLR 2025
In-Context Unlearning: Language Models as Few-Shot Unlearners
ICML 2024
I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data
AAAI 2024
Gaussian Membership Inference Privacy
NeurIPS 2023
Language Models are Realistic Tabular Data Generators
ICLR 2023
On the Privacy Risks of Algorithmic Recourse
AISTATS 2023
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
ICLR 2023
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
ICLR 2023
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
AISTATS 2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
NeurIPS 2022
On Counterfactual Explanations under Predictive Multiplicity
UAI 2020