基本信息
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职业迁徙
个人简介
My main research interests are in:
Interpretable Machine Learning. My research has focused on tree-based methods, where I have worked on both developing inherently interpretable algorithms (e.g., shallow decision trees) and post-hoc explanation methods for tree ensembles such as Random Forests and XGBoost. Recently, I have been thinking about how to apply these tools to interpret deep learning and large language models.
Interpretable Machine Learning. My research has focused on tree-based methods, where I have worked on both developing inherently interpretable algorithms (e.g., shallow decision trees) and post-hoc explanation methods for tree ensembles such as Random Forests and XGBoost. Recently, I have been thinking about how to apply these tools to interpret deep learning and large language models.
研究兴趣
论文共 8 篇作者统计合作学者相似作者
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期刊级别
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CoRR (2024)
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arXiv (Cornell University) (2023)
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2
0
medRxiv (Cold Spring Harbor Laboratory) (2023)
J. Open Source Softw.no. 69 (2022)
arxiv(2022)
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作者统计
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D-Core
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