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actionable-recourse

evaluate recourse in linear classification models

apmla_material

Material for the Course on Advanced Probabilistic ML and Applications

c-chvae

carla

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

counterfactualdistanceattack

"On the Privacy Risks of Algorithmic Recourse". Martin Pawelczyk, Himabindu Lakkaraju* and Seth Neel*. In International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2023.

in-context-unlearning

"In-Context Unlearning: Language Models as Few Shot Unlearners". Martin Pawelczyk, Seth Neel* and Himabindu Lakkaraju*; arXiv preprint: arXiv:2310.07579; 2023.

openxai

OpenXAI : Towards a Transparent Evaluation of Model Explanations

probabilisticallyrobustrecourse

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness. M. Pawelczyk, T. Datta, J. v.d Heuvel, G. Kasneci, H. Lakkaraju. International Conference on Learning Representations 2023 (ICLR).

road_evaluation

Source Code of the ROAD benchmark for feature attribution methods

run_llama2-7b-1gpu