TRIPODS+X: RES: Collaborative Research: Scaling Up Descriptive Epidemiology and Metabolic Network Models via Faster Sampling

TRIPODS X：RES：协作研究：通过更快的采样扩大描述性流行病学和代谢网络模型

基本信息

批准号：
1839323
负责人：
Santosh Vempala
金额：
$ 12万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839323&HistoricalAwards=false
关键词：
TRIPODS+X RES Collaborative Research Scaling

项目摘要

Sampling from a given distribution from a space with many attributes is a fundamental problem in computer science. Over the past two decades, practical applications of sampling have proliferated in areas such as statistics, networking, biology, differential privacy, and, most notably, machine learning. Sampling is used to evaluate models, as a subroutine for optimization, and more generally for exploring large complex spaces. In these practical settings, the time complexity of sampling is a severe limitation; known methods often require either restricting sampling to very small instances or resorting to unproven heuristics or overly restrictive assumptions. This project will develop a toolkit for sampling and evaluate it on real data sets --- a large-scale, high-dimensional toolkit for sampling smooth and non-smooth distributions, and a suite of functions that can be computed or estimated using access to samples. It will be developed working together with domain experts in health metrics and systems biology.The overall goal of the project is to produce a general-purpose, open-source, and publicly accessible software for sampling non-smooth log-concave distributions with millions of variables. Achieving these goals requires overcoming complex challenges in both theory and implementation. The new algorithms will be inspired by the investigators' expertise in convex optimization, high dimensional geometry, and randomized linear algebra, especially their breakthroughs in linear programming and volume computation. In both target application domains, health metrics and systems biology, the investigators have worked with experts to develop the current state-of-the-art software tools and deployed them. Drawing from this experience, they are poised to both develop general tools and make data-driven discoveries in these domains.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.

从具有许多属性的空间中对给定分布进行采样是计算机科学中的一个基本问题。在过去的二十年中，采样的实际应用在统计、网络、生物学、差异隐私以及最引人注目的机器学习等领域激增。采样用于评估模型，作为优化的子程序，更一般地用于探索大型复杂空间。在这些实际设置中，采样的时间复杂度是一个严重的限制；已知的方法通常需要将采样限制在非常小的实例中，或者诉诸未经证实的启发法或过度限制性的假设。该项目将开发一个用于采样并在真实数据集上对其进行评估的工具包——一个用于对平滑和非平滑分布进行采样的大规模、高维工具包，以及一组可以使用访问来计算或估计的函数样品。它将与健康指标和系统生物学领域的专家合作开发。该项目的总体目标是开发一个通用的、开源的、可公开访问的软件，用于对数百万个数据的非平滑对数凹分布进行采样。变量。实现这些目标需要克服理论和实施方面的复杂挑战。新算法将受到研究人员在凸优化、高维几何和随机线性代数方面的专业知识的启发，特别是他们在线性规划和体积计算方面的突破。在健康指标和系统生物学这两个目标应用领域，研究人员与专家合作开发了当前最先进的软件工具并进行了部署。借鉴这一经验，他们准备在这些领域开发通用工具并做出数据驱动的发现。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。