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How Interpretable and Trustworthy are GAMs?

, , , , и . KDD, стр. 95-105. ACM, (2021)

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Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models., , , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 2402-2412. PMLR, (2020)Interpretable Personalized Experimentation., , , , , , , , , и . KDD, стр. 4173-4183. ACM, (2022)Interpretable Approaches to Detect Bias in Black-Box Models.. AIES, стр. 382-383. ACM, (2018)Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data., , , , , и . PKDD/ECML Workshops (1), том 1524 из Communications in Computer and Information Science, стр. 534-551. Springer, (2021)Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism., , , , , и . CHI, стр. 1-13. ACM, (2020)Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models., , , , , и . CoRR, (2018)Axiomatic Interpretability for Multiclass Additive Models., , , , , и . KDD, стр. 226-234. ACM, (2019)How Interpretable and Trustworthy are GAMs?, , , , и . KDD, стр. 95-105. ACM, (2021)MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases., , , , , , , , , и 8 other автор(ы). CoRR, (2024)Transparent Model Distillation., , , и . CoRR, (2018)