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Titlebook: Job Security and Flexibility; Exploring Labour Mar Emil Dinga,Monica Florica Dutcas,Gabriela-Mariana Book 2023 The Editor(s) (if applicabl

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發(fā)表于 2025-3-23 10:24:58 | 只看該作者
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發(fā)表于 2025-3-23 17:42:26 | 只看該作者
https://doi.org/10.1007/978-3-031-28509-7Job security; Flexibility in the job market; Economic rigidities; Labour market in Romania; Labour marke
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發(fā)表于 2025-3-23 19:07:31 | 只看該作者
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發(fā)表于 2025-3-24 03:36:35 | 只看該作者
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發(fā)表于 2025-3-24 09:55:54 | 只看該作者
Emil Dinga,Monica Florica Dutcas,Gabriela-Mariana Ionescue background data is formed. We find that SHAP for RL requires only modest amounts of background data and that clustering is preferred over sampling as a selection method. (2) Additionally, we analyze how SHAP-based feature importance relates to overall agent performance (return). We find that while
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發(fā)表于 2025-3-24 11:33:18 | 只看該作者
Emil Dinga,Monica Florica Dutcas,Gabriela-Mariana Ionescuhanges in conformal metrics such as set size, coverage, and thus formation of prediction set-type outputs. Our paper introduces a comprehensive global explainability framework based on conformal inference, addressing the void of accommodating prediction set-type outputs in various classifiers. This
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發(fā)表于 2025-3-24 16:38:37 | 只看該作者
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發(fā)表于 2025-3-24 22:14:59 | 只看該作者
Emil Dinga,Monica Florica Dutcas,Gabriela-Mariana Ionescung explanation similarity, determining token importance and prediction change metrics, the level of human intervention required, and the creation of suitable test datasets. The paper illustrates how these challenges can be intertwined, showcasing new opportunities for the community. These include de
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發(fā)表于 2025-3-25 01:06:35 | 只看該作者
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