Robust Classification and uncertainty quantification for non-iid samples

尽管机器学习的进步在各种应用中显著提高了分类精度，但最近的研究已经将重点扩展到仅仅预测精度。现在人们越来越重视对预测不确定性进行稳健量化的必要性，在处理异常样本时的自我意识，以及分类模型推广到小群体或新群体的能力。在这些环境中，传统的独立同分布(IID)数据生成假设不再成立，解决这些环境中的挑战，对于在医疗诊断或政策制定等安全和公平关键系统中有效应用机器学习技术至关重要。该项目还将通过学生参与研究来促进对他们的培训。该项目旨在通过使用分布稳健优化、公平性学习、保角预测和半监督学习等技术，提出在复杂的非IID环境下推断类别标签和量化不确定性的稳健方法。该项目将提出创新的分类战略，这些战略将改善潜在亚群体中最差群体的表现，并增强潜在敏感属性的公平性。这些策略将与最先进的机器学习技术相结合，包括神经网络和梯度提升，以开发健壮、可推广和灵活的机器学习算法。此外，该项目还将开发新的自适应分类策略，并研究其理论保证。这些策略将利用已标记的训练数据和未标记的测试样本。最后，该项目旨在促进开发的方法在安全关键和公平关键系统中的深入应用，特别是在医学和免疫学研究领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.