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AF:Medium:Collaborative Research:Estimation, Learning, and Memory: The Quest for Statistically Optimal Algorithms

AF：中：协作研究：估计、学习和记忆：追求统计最优算法

基本信息

批准号：
1704417
负责人：
Gregory Valiant
金额：
$ 55万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704417&HistoricalAwards=false
关键词：
AF Medium Collaborative Research Estimation

项目摘要

The goal of this project is to develop new, efficient algorithms that extract as much information as is possible from a given quantity of data. In particular, this research aims to develop an understanding of how to leverage structure that is present in natural language settings, medical and genomic settings, and network- or graph-based settings. Many fundamental types of structure are encountered repeatedly in widely varying scientific and technological settings; our goal is to build on a recent body of work that focused on the simplest unstructured settings, and develop broadly applicable tools and insights to these diverse settings. A central component of this project is a close interaction and transfer of ideas, problems, and techniques, between the theory community, the machine learning community, and the broader set of data-centric researchers and practitioners.From a technical perspective, this research focuses on three fundamental types of structure: geometric structure, algebraic or low-rank structure, and the structure that is present in sequentialdata (such as natural language). For the first two types of structure, the research focus is on understanding the possibilities and limitations in the sparse data regime where the amount of data is comparable to, or sublinear in, the dimensionality of the data. In the third setting, the focus is on understanding the role of memory for learning and prediction tasks.Beyond the direct research goals of the project, the PIs are extensively involved in teaching and outreach, including designing UW?s new data sciences curriculum, and developing new courses on algorithms and foundational aspects of data sciences at Stanford.

该项目的目标是开发新的，有效的算法，从给定数量的数据中提取尽可能多的信息。特别是，这项研究旨在了解如何利用自然语言环境，医学和基因组环境以及基于网络或图形的环境中存在的结构。许多基本类型的结构在各种各样的科学和技术环境中反复遇到;我们的目标是建立在最近的工作，专注于最简单的非结构化设置，并开发广泛适用的工具和见解，这些不同的设置。该项目的一个核心组成部分是理论社区、机器学习社区和更广泛的以数据为中心的研究人员和实践者之间的思想、问题和技术的密切互动和转移。从技术角度来看，本研究侧重于三种基本类型的结构：几何结构、代数结构或低秩结构，以及序列数据（如自然语言）中存在的结构。对于前两种类型的结构，研究的重点是了解稀疏数据制度的可能性和局限性，其中数据量与数据的维度相当或呈次线性。在第三种情况下，重点是了解记忆在学习和预测任务中的作用。除了项目的直接研究目标，PI广泛参与教学和推广，包括设计UW？的新数据科学课程，并在斯坦福大学开发关于算法和数据科学基础方面的新课程。