Uncertainty Quantification for Machine Learning

计算机正在越来越多地做出影响到我们每一个人的决定，在广泛的场景中，如决定信用卡限额、驾驶飞机、预测天气和通过互联网推荐产品。执行这些任务的算法是复杂的，它们的复杂性不能被所有依赖它们的人完全理解。该项目的目标是为这些决策算法配备不确定性措施，以便在适当的情况下，可以使用人工或进一步的计算机干预来确保公平和安全的结果。参与该项目的初级研究人员还将参与由加州理工学院组织的外展项目。这些外展项目针对的是高中生，并特别设计用于影响各种各样的高中生；这项外联工作将通过为日常算法配备不确定性测量的研究经验得到加强。这个项目的目的是为了更深入地了解机器学习算法。这将通过以统计方式制定和解决问题来实现，在统计方式中，用于学习的数学模型和用于训练它们的数据中的不确定性都被跟踪和量化。目标有两个：(i)通过允许现有算法认识到自身的不确定性来改进现有算法，或者通过允许人类以知情的方式与它们互动；（ii）利用不确定性知识来研究算法的预测能力，并识别其中隐含编码的法律或规则。将采用一系列机器学习任务的贝叶斯公式，重点关注神经网络，以及基于图的半监督学习中出现的相关问题。本文将介绍高维蒙特卡罗马尔可夫链（MCMC）采样器的最新进展，对不确定性进行实证研究。各种参数限制（包括大数据量、高维空间中的数据和小数据噪声）将用于发展数学理论，量化机器学习算法预测中的不确定性。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。

Computers are increasingly making decisions that impact on each and every one of us, in scenarios as wide-ranging as deciding credit card limits, flying aircraft, predicting weather and recommending products via the internet. The algorithms which perform these tasks are complex and their intricacies cannot be fully appreciated by all the people who rely on them. The goal of this project is to equip these decision-making algorithms with measures of uncertainty so that, where appropriate, human or further computer intervention can be used to ensure fair and safe outcomes. The junior researchers involved in the program will also engage in outreach programs, organized through Cal Tech. These outreach programs are aimed at high school students, and designed in particular to impact on a diverse range of high school students; this outreach work will be enhanced by experience with the research about equipping everyday algorithms with measures of uncertainty.The purpose of this project is to obtain a deeper understanding of machine learning algorithms. This will be achieved by formulating and solving the problems in a statistical fashion in which uncertainty in both the mathematical models used for learning, and the data used to train them, is tracked and quantified. The objectives are twofold: (i) to improve existing algorithms by allowing them to be cognizant of their own uncertainties, or by allowing humans to interact with them in an informed fashion; (ii) to use knowledge of uncertainties to study the predictive power of the algorithms and identify laws or rules implicitly encoded within them. A Bayesian formulation of a number of machine learning tasks will be adopted, with focus on neural networks, and related issues arising in graph-based semi-supervised learning. Recent advances in the development of Monte Carlo Markov chain (MCMC) samplers in high dimensions will be deployed to make empirical studies of uncertainty. Various parameter limits (including large data volume, data in high dimensional spaces, and small data noise) will be used to develop mathematical theories which quantify uncertainty in the predictions made by machine learning algorithms.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.