Robust Classification and uncertainty quantification for non-iid samples

尽管机器学习的进步显著提高了各种应用中的分类精度，但最近的研究已经将重点扩展到不仅仅是预测精度。现在越来越强调对预测不确定性进行稳健量化的必要性，处理异常样本的自我意识，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，传统的独立和同分布（IID）数据生成假设不再成立，解决这些挑战对于有效地将机器学习技术应用于安全关键和公平关键系统（如医疗诊断或政策制定）至关重要。该项目还将通过学生参与研究对他们进行培训。该项目旨在通过采用分布鲁棒优化、公平学习、保形预测和半监督学习等技术，推进在复杂的非iid设置下推断类标签和量化不确定性的鲁棒方法。该项目将提出创新的分类策略，在潜在的亚群体中表现出更好的最差群体表现，并在潜在的潜在敏感属性方面增强公平性。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增强，以开发鲁棒、可推广和灵活的机器学习算法。此外，该项目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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.