Collaborative Research: AF: Small: Phase Transitions in Sampling Related Problems

合作研究：AF：小：采样相关问题中的相变

• 批准号: 2205743
• 负责人: Eric Vigoda
• 金额: $ 24.99万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205743&HistoricalAwards=false
• 关键词: Collaborative; Research; AF; Small; Phase

Graphical models are a widely used tool to succinctly represent high-dimensional data and to understand the fundamental building blocks and interactions of physical and biological systems. These models have appeared in a variety of scientific fields. In physics they are used to understand the thermodynamic properties of ferromagnetic materials and are integral in the study of phase transitions in physical systems. In biology these models are a fundamental tool for inferring evolutionary history using genetic data in phylogenetic models. Graphical models are ubiquitous in machine learning for computational tasks such as Bayesian inference. This project addresses fundamental computational tasks that are crucial for studying, constructing, and utilizing graphical models. The project will involve undergraduate students in research involving graphical models in social science settings.There are two core tasks for studying graphical models: learning and sampling. The learning problem is focused on inferring the inner structure of the underlying graphical model from the macroscopic behavior of the system. In contrast, the goal of the associated sampling problem is to efficiently simulate the thermodynamic behavior of a learned or inferred graphical model. This project will develop new algorithms, and more generally understand the computational complexity of sampling and learning as well as several related problems. A common theme in this project is connecting the computational complexity of these sampling- and inference-related problems with statistical-physics phase transitions.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.

图形模型是一种广泛使用的工具，可以简洁地表示高维数据，并理解物理和生物系统的基本构建块和相互作用。这些模型已经出现在各种科学领域。 在物理学中，它们用于理解铁磁材料的热力学性质，并且在物理系统的相变研究中不可或缺。在生物学中，这些模型是使用系统发育模型中的遗传数据推断进化历史的基本工具。 图形模型在机器学习中无处不在，用于计算任务，如贝叶斯推理。 该项目解决了对学习，构建和利用图形模型至关重要的基本计算任务。 该项目将涉及本科生在研究涉及图形模型在社会科学设置。有两个核心任务，研究图形模型：学习和采样。 学习问题的重点是从系统的宏观行为推断底层图形模型的内部结构。相比之下，相关采样问题的目标是有效地模拟学习或推断的图形模型的热力学行为。 这个项目将开发新的算法，更普遍地理解采样和学习的计算复杂性以及几个相关的问题。该项目的一个共同主题是将这些采样和推理相关问题的计算复杂性与物理相变联系起来。该奖项反映了NSF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。

项目成果

Spectral Independence via Stability and Applications to Holant-Type Problems

• DOI: 10.1109/focs52979.2021.00023
• 发表时间: 2021-06
• 期刊: 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)
• 作者: Zongchen Chen;Kuikui Liu;Eric Vigoda

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

• 发表时间: 2022-07
• 作者: Antonio Blanca;Zongchen Chen;Daniel Stefankovic;Eric Vigoda

Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models

受限玻尔兹曼机和 Potts 模型的身份测试硬度

• 发表时间: 2021
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Blanca, A;Chen, Z;Štefankovič, D;Vigoda, E.
• 通讯作者: Vigoda, E.

Approximating Observables Is as Hard as Counting

近似可观测值与计数一样困难

• 发表时间: 2022
• 期刊: ICALP 2022
• 作者: Galanis, Andreas;Štefankovič, Daniel;Vigoda, Eric
• 通讯作者: Vigoda, Eric

Counting and Sampling Labeled Chordal Graphs in Polynomial Time

• DOI: 10.48550/arxiv.2308.09703
• 发表时间: 2023-08
• 期刊: ArXiv
• 作者: Úrsula Hébert-Johnson;D. Lokshtanov;Eric Vigoda