Robust Classification and uncertainty quantification for non-iid samples

非独立同分布样本的稳健分类和不确定性量化

• 批准号: 2310836
• 负责人: Leying Guan
• 金额: $ 16万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310836&HistoricalAwards=false
• 关键词: Robust; Classification; uncertainty; quantification; non

Although advancements in machine learning have significantly improved classification accuracy in various applications, recent research has expanded the focus beyond solely prediction accuracy. There is now an increased emphasis on the necessity for robust quantification of prediction uncertainty, self-awareness in handling abnormal samples, and the ability of classification models to generalize to minor or novel populations. Addressing challenges in these settings, where the traditional independent and identically distributed (IID) data generating assumption no longer holds, is crucial for effectively applying machine learning techniques in safety-critical and fairness-critical systems, such as medical diagnosis or policy making. The project will also contribute to the training of students through their involvement in the research. This project aims to advance robust methods for inferring class labels and quantifying uncertainty under complex non-IID settings by employing techniques from distributionally robust optimization, fairness learning, conformal prediction, and semi-supervised learning. The project will propose innovative classification strategies that exhibit improved worst-group performance across latent sub-populations and enhanced fairness with respect to potentially latent sensitive attributes. These strategies will be integrated with state-of-the-art machine learning techniques, including neural networks and gradient boosting, to develop robust, generalizable, and flexible machine learning algorithms. Additionally, the project will develop novel adaptive classification strategies and investigate their theoretical guarantees. These strategies will leverage both labeled training data and unlabeled test samples. Finally, the project aims to facilitate the in-depth application of the developed methods in safety-critical and fairness-critical systems, particularly in the domains of medical and immunological studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管机器学习的进步在各种应用程序中已大大提高了分类精度，但最近的研究将重点扩大到了仅仅预测准确性之外。现在，人们越来越重视预测不确定性，处理异常样本中的自我意识以及分类模型推广到次要或新型人群的能力的必要性。解决这些环境中的挑战，在这种情况下，传统的独立和相同分布（IID）的数据生成假设不再存在，对于有效地将机器学习技术应用于安全至关重要的关键性和关键性系统（例如医学诊断或政策制定）至关重要。该项目还将通过参与研究来为学生培训。该项目旨在通过采用分布强大的优化，公平性学习，结构性预测和半义务学习的技术来推动强大的方法来推断班级标签和量化复杂非IID设置下的不确定性。该项目将提出创新的分类策略，这些策略在潜在的潜在敏感属性方面表现出了在潜在的亚种群中提高了最差的群体表现，并增强了公平性。这些策略将与最先进的机器学习技术（包括神经网络和梯度提升）集成，以开发可靠，可推广和灵活的机器学习算法。此外，该项目将制定新颖的自适应分类策略并研究其理论保证。这些策略将利用标记的培训数据和未标记的测试样本。最后，该项目旨在促进开发方法在安全至关重要和关键性系统中的深入应用，尤其是在医学和免疫学研究领域。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。

项目成果

Smooth and probabilistic PARAFAC model with auxiliary covariates

• DOI: 10.1080/10618600.2023.2257783
• 发表时间: 2021-04
• 期刊: Journal of Computational and Graphical Statistics
• 影响因子: 2.4
• 作者: Leying Guan