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.

虽然机器学习的进步已经显著提高了各种应用中的分类准确性，但最近的研究已经将重点扩展到预测准确性之外。现在越来越强调对预测不确定性的稳健量化、处理异常样本的自我意识以及分类模型推广到次要或新人群的能力的必要性。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，解决这些挑战对于在安全关键和公平关键系统（如医疗诊断或政策制定）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，为培训学生作出贡献。该项目旨在通过采用分布式鲁棒优化，公平学习，保形预测和半监督学习等技术，在复杂的非IID设置下，推进用于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，表现出改进的最差群体的性能在潜在的子群体和增强的公平性方面的潜在的敏感属性。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）相结合，以开发强大，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，该项目旨在促进所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，并被认为值得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来支持。

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