梳理机器学习与稳健统计中弱结构信息下的决策理论基础
Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information
- 整合十年研究成果，构建弱结构信息下的决策理论框架
- 提出多篇论文的核心思想，形成系统性研究体系
- 适合对理论机器学习和统计推断感兴趣的学者阅读
本任教资格论文为综合性成果，汇集了作者（及多位合作者）近年的研究工作。核心内容由第5页列出的十项贡献组成，每项均对应一篇完整论文，参考文献可直接查阅。后续章节A至C及结论部分旨在将这些论文置于更广泛的科学背景中，以非正式方式简要说明各研究内容，并指出各领域未来研究的潜在方向。因此，本文不追求论文级别的细节与形式严谨性，而是致力于为读者提供该重要研究领域的高层概览，以向更广泛受众推广这一关键领域。
原文摘要 · Abstract (English)
This habilitation thesis is cumulative and, therefore, is collecting and connecting research that I (together with several co-authors) have conducted over the last few years. Thus, the absolute core of the work is formed by the ten publications listed on page 5 under the name Contributions 1 to 10. The references to the complete versions of these articles are also found in this list, making them as easily accessible as possible for readers wishing to dive deep into the different research projects. The chapters following this thesis, namely Parts A to C and the concluding remarks, serve to place the articles in a larger scientific context, to (briefly) explain their respective content on a less formal level, and to highlight some interesting perspectives for future research in their respective contexts. Naturally, therefore, the following presentation has neither the level of detail nor the formal rigor that can (hopefully) be found in the papers. The purpose of the following text is to provide the reader an easy and high-level access to this interesting and important research field as a whole, thereby, advertising it to a broader audience.
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