CAREER: Automatic Variational Inference

职业：自动变分推理

• 批准号: 2045900
• 负责人: Justin Domke
• 金额: $ 55.08万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045900&HistoricalAwards=false
• 关键词: CAREER; Automatic; Variational; Inference

Hundreds of thousands of people across science, government, and business use automatic probabilistic inference tools. In these tools, people carefully state their assumptions, and then combine them with observed data. However, these automatic tools only work at a relatively modest scale, and ever-growing datasets require more powerful methods. Recent years have seen the development of a novel strategy for inference that has been able to address data orders of magnitude larger. However, great care and expertise is needed to wield these methods successfully, putting them out of reach for most potential users. This project seeks to promote the progress of science by making these large-scale techniques more automatic, putting them within reach of the vast majority of users not able to invest huge amounts of effort in manual algorithmic engineering.This project advances methodology for automatic and general-purpose variational inference, with the goal of answering two questions. The first question is when does variational inference work. This is paramount, since no method can succeed on all problems. We take three directions, namely new diagnostic error measures, improved scalability for diagnostics, and an empirical evaluation on a corpus of real non-expert models gathered from an integrated course. The second question is how to automate algorithmic design choices. Variational inference algorithms require many delicate design choices, currently made manually. The core idea to automate these decisions is to maintain statistics so the effect of any set of choices on optimization speed can be predicted. This project will contribute 1) a corpus and evaluation of automatic inference on non-expert models, 2) improved diagnostic performance measures, and 3) methods to automatically make variational inference choices, guided by convergence rates.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.

跨越科学，政府和商业的成千上万人使用自动概率推理工具。在这些工具中，人们仔细说明了他们的假设，然后将它们与观察到的数据相结合。但是，这些自动工具只能以相对较小的量表起作用，而不断增长的数据集则需要更强大的方法。近年来，已经发展了一种新的推理策略，该策略能够解决更大的数量级。但是，需要大量的护理和专业知识才能成功运用这些方法，从而使它们无法触及大多数潜在用户。该项目旨在通过使这些大规模技术更自动来促进科学的进步，从而使它们无法在绝大多数用户范围内投入到手动算法工程上。这项项目进步方法是自动和通用式变种推断的方法，目的是回答两个问题。第一个问题是变异推理何时起作用。这是至关重要的，因为没有任何方法能够在所有问题上取得成功。我们采用三个方向，即新的诊断错误指标，提高诊断的可伸缩性以及对从集成课程收集的实际非专家模型的语料库进行的经验评估。第二个问题是如何自动化算法设计选择。各种推理算法需要许多精致的设计选择，目前是手动做出的。自动执行这些决定的核心思想是维护统计数据，以便可以预测任何一组选择对优化速度的影响。该项目将贡献1）对非专家模型的自动推断的语料库和评估，2）改进的诊断性能度量和3）3）通过融合率指导的方法自动做出变异推理选择的方法。该奖项反映了NSF的立法任务，并通过使用基础的知识效果和广泛的评估来评估，并值得通过评估来进行评估。