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Activity Number:
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408
- Recent Advances in Statistical Machine Learning
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract #320835
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Title:
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Optimal and Safe Estimation for High-Dimensional, Semi-Supervised Learning
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Author(s):
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Yang Ning*
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Companies:
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Cornell University
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Keywords:
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High dimensionality;
Minimax optimality;
semi-supervised learning
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Abstract:
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There are many scenarios such as the electronic health records where the outcome is much more difficult to collect than the covariates. In this paper, we consider the linear regression problem with such a data structure under the high dimensionality. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation and inference of the regression parameters in linear models, especially in light of the fact that such linear models may be misspecified in data analysis. In particular, we address the following two important questions. (1) Can we use the labeled data as well as the unlabeled data to construct a semi-supervised estimator such that its convergence rate is faster than the supervised estimators? (2) Can we construct confidence intervals or hypothesis tests that are guaranteed to be more efficient or powerful than the supervised estimators?
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Authors who are presenting talks have a * after their name.
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