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

, , , , and . NeurIPS Datasets and Benchmarks, (2021)

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OpenXAI: Towards a Transparent Evaluation of Model Explanations., , , , , , , and . NeurIPS, (2022)Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach., , and . IEEE BigData, page 768-778. IEEE, (2021)Machine Unlearning Fails to Remove Data Poisoning Attacks., , , , , and . CoRR, (2024)On the Trade-Off between Actionable Explanations and the Right to be Forgotten., , , and . ICLR, OpenReview.net, (2023)CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms., , , , and . NeurIPS Datasets and Benchmarks, (2021)On the Privacy Risks of Algorithmic Recourse., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 9680-9696. PMLR, (2023)Language Models are Realistic Tabular Data Generators., , , , and . ICLR, OpenReview.net, (2023)In-Context Unlearning: Language Models as Few-Shot Unlearners., , and . ICML, OpenReview.net, (2024)In-Context Unlearning: Language Models as Few Shot Unlearners., , and . CoRR, (2023)Learning Model-Agnostic Counterfactual Explanations for Tabular Data., , and . WWW, page 3126-3132. ACM / IW3C2, (2020)