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主讲人：
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Ben Dai |
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主讲人简介：
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Dr. Ben Dai an Assistant Professor in the Department of Statistics at The Chinese University of Hong Kong. His primary research interests include statistical consistency, theory-driven machine learning methods, theoretical foundation of machine learning, black-box significance testing, statistical computing and software development. |
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主持人：
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王晨笛 |
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简介：
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An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with a black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretations of the decision-making process based on a deep learning model. However, testing for a neural network poses a challenge because of its black-box nature and unknown limiting distributions of parameter estimates while existing methods require strong assumptions or excessive computation. In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type, such as an image. The one-split test estimates and evaluates a black-box model based on estimation and inference subsets through sample splitting and data perturbation. The two-split test further splits the inference subset into two but requires no perturbation. Also, we develop their combined versions by aggregating the p -values based on repeated sample splitting. By deflating the bias-sd-ratio, we establish asymptotic null distributions of the test statistics and the consistency in terms of Type 2 error. Numerically, we demonstrate the utility of the proposed tests on seven simulated examples and six real datasets. Accompanying this article is our python library dnn-inference (https://dnn-inference.readthedocs.io/en/latest/) that implements the proposed tests. |
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时间：
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2025-03-07 (Friday) 16:40-18:00 |
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地点：
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经济楼N302 |
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期数：
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主办单位：
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厦门大学经济学院、王亚南经济研究院、邹至庄经济研究院 |
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承办单位：
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厦门大学经济学院统计学与数据科学系 |
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类型：
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独立讲座 |
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联系人信息：
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zmn1994@xmu.edu.cn |
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语言：
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中文 |
