Algorithmic High-Dimensional Robust Statistics

高维稳健统计算法

计算机科学技术基础学科

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作      者
出  版 社
出版时间
2023年08月01日
装      帧
精装
ISBN
9781108837811
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页      码
300
开      本
229x152mm
语      种
英文
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图书简介
Robust Statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as model misspecification or adversarial outliers in the dataset. The classical statistical theory, dating back to pioneering works by Tukey and Huber, characterizes the information-theoretic limits of robust estimation for most common problems. A recent line of work in computer science gave the first computationally efficient robust estimators in high dimensions for a range of learning tasks. This reference text for graduate students, researchers, and professionals in machine learning theory, provides an overview of recent developments in algorithmic high-dimensional robust statistics, presenting the underlying ideas in a clear and unified manner, while leveraging new perspectives on the developed techniques to provide streamlined proofs of these results. The most basic and illustrative results are analyzed in each chapter, while more tangential developments are explored in the exercises.
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