AF: Small: Integrated Knowledge Discovery and Analysis Using Sum-of-Squares Proofs

AF：小：使用平方和证明进行综合知识发现和分析

基本信息

批准号：
1718380
负责人：
Brendan Juba
金额：
$ 44万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718380&HistoricalAwards=false
关键词：
AF Small Integrated Knowledge Discovery

项目摘要

Developing approaches to subjects as diverse as advertising, bioinformatics, counterterrorism, fraud detection, politics, sociology, and so on are based on data analysis. This project will develop new algorithms for data analysis with strong guarantees of their correctness and efficiency. Tools based on these algorithms will be usable as-is, without expert knowledge, in innovative data-driven applications far removed from academia. The project will also involve the training of students in advanced techniques for data analysis.The algorithms considered in this project involve automated reasoning with an algebraic logic known as "sum-of-squares." Automated reasoning requires a delicate trade-off between expressiveness and simplicity, to facilitate reasoning that is both fast and effective. Sum-of-squares is capable of expressing much statistical reasoning, and yet is sufficiently simple to allow the design of tractable algorithms for reasoning. This project will consider how these algorithms can be used to reason about an overall distribution or population from a sample of data drawn from it. The main aim of the project is to develop efficient algorithms that guarantee that all of the relevant statistical facts are discovered during data analysis. The project will further develop these algorithms to solve problems in domains such as Computer Vision and Natural Language Processing.In addition to the development of domain-specific algorithms, the project will consider sum-of-squares reasoning with high-degree polynomials. Although such reasoning in the standard sense is intractable, the project aims to simulate reasoning with such high-degree expressions with the aid of the sample of data from the distribution to be reasoned about. The project will also investigate the expressive power of sum-of-squares with such high-degree expressions. Specifically, the project will investigate whether or not such reasoning can simulate other logics such as resolution (or vice-versa), and will further investigate the extent of its ability to basic capture statistical notions.

广告、生物信息学、反恐、欺诈检测、政治、社会学等多种学科的开发方法都是基于数据分析的。该项目将开发新的数据分析算法，并有力保证其正确性和效率。基于这些算法的工具将可以在远离学术界的创新数据驱动应用程序中按原样使用，无需专业知识。该项目还将涉及对学生进行高级数据分析技术的培训。该项目中考虑的算法涉及使用称为“平方和”的代数逻辑进行自动推理。自动推理需要在表达性和简单性之间进行微妙的权衡，以促进快速有效的推理。平方和能够表达很多统计推理，而且足够简单，可以设计易于处理的推理算法。该项目将考虑如何使用这些算法从数据样本中推断总体分布或总体。该项目的主要目标是开发有效的算法，确保在数据分析过程中发现所有相关的统计事实。该项目将进一步开发这些算法来解决计算机视觉和自然语言处理等领域的问题。除了开发特定领域的算法外，该项目还将考虑使用高次多项式进行平方和推理。尽管这种标准意义上的推理很棘手，但该项目的目标是借助要推理的分布中的数据样本来模拟这种高级表达式的推理。该项目还将研究具有如此高阶表达式的平方和的表达能力。具体来说，该项目将调查这种推理是否可以模拟其他逻辑，例如解析（或反之亦然），并将进一步调查其基本捕获统计概念的能力程度。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Safe Partial Diagnosis from Normal Observations

根据正常观察进行安全的部分诊断

DOI：
10.1609/aaai.v33i01.33013084
发表时间：
2019-01
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Stern, Roni;Juba, Brendan
通讯作者：
Juba, Brendan

Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions

具有线性值函数的分解状态 MDP 中的多项式时间强化学习

DOI：
发表时间：
2021-07-12
期刊：
影响因子：
0
作者：
Zihao Deng;Siddartha Devic;Brendan Juba
通讯作者：
Brendan Juba

One network fits all? Modular versus monolithic task formulations in neural networks

一个网络适合所有人？

DOI：
发表时间：
2021-01
期刊：
Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021
影响因子：
0
作者：
Agarwala, Atish;Das, Abhimanyu;Juba, Brendan;Panigrahy, Rina;Sharan, Vatsal;Wang, Xin;Zhang, Qiuyi
通讯作者：
Zhang, Qiuyi

Conditional Linear Regression

条件线性回归