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  • title: Scalable Rule-Based Representation Learning for Interpretable Classification
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            Scalable Rule-Based Representation Learning for Interpretable Classification
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            Scalable Rule-Based Representation Learning for Interpretable Classification

            Dec 6, 2021

            Speakers

            ZW

            Zhuo Wang

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            WZ

            Wei Zhang

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            NL

            Ning Liu

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            About

            Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a n…

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            NeurIPS 2021

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