CAREER: Statistical Inference Under Information Constraints: Efficient Algorithms and Fundamental Limits

职业：信息约束下的统计推断：高效算法和基本限制

基本信息

批准号：
1846300
负责人：
Jayadev Acharya
金额：
$ 55.27万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846300&HistoricalAwards=false
关键词：
CAREER Statistical Inference Under Information

项目摘要

Data science and machine learning systems have to optimize constraints on the availability of data, computation time, memory for storage, and privacy concerns. For example, while performing web search on mobile devices, one would like the applications to be small in size, communicate as little data as possible, and leak as little about the user as possible. These constraints are often at odds with each other. A system that provides strong privacy guarantees might require more data and computation, and a system that uses little data might require more computation. A fundamental understanding of the limits and trade-offs between constrained resources such as samples, time, memory, communication, and privacy is critical for tackling the many challenges in data science that lay ahead. In spite of many success stories of data science, these trade-offs are poorly understood even in some of the simplest settings. This project aims to establish the fundamental trade-offs between these resources, as well as design efficient schemes that achieve them. The project outcomes can help design faster, communication-frugal, privacy-preserving, and space-efficient learning systems. The project seeks to involve the participation of a diverse group of researchers in this project through outreach activities that target undergraduate students and under-represented communities.The investigator will formulate and study fundamental statistical inference tasks such as distribution estimation, hypothesis testing, and distribution property estimation under the information constraints mentioned above. A particular direction of interest is the impact of the availability of shared randomness on the other constraints for distributed machine learning systems. While the role of randomness has been studied in problems in communication complexity, its role in machine learning systems is often overlooked. The project will integrate ideas from computer science, information theory, machine learning, and statistics, seeking to bridge researchers from these communities. All findings of this project will be disseminated through publications, and will be made publicly available on the investigator's website.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.

数据科学和机器学习系统必须优化对数据可用性、计算时间、存储内存和隐私问题的限制。例如，当在移动设备上执行网络搜索时，人们希望应用程序尺寸较小、通信的数据尽可能少，并且泄露的用户信息也尽可能少。这些限制常常相互矛盾。提供强大隐私保证的系统可能需要更多的数据和计算，而使用很少数据的系统可能需要更多的计算。对样本、时间、内存、通信和隐私等受限资源之间的限制和权衡有一个基本的了解，对于解决数据科学中面临的许多挑战至关重要。尽管数据科学有许多成功的案例，但即使在一些最简单的环境中，人们对这些权衡也知之甚少。该项目旨在建立这些资源之间的基本权衡，并设计实现这些资源的有效方案。该项目成果可以帮助设计更快、通信节约、保护隐私和节省空间的学习系统。该项目旨在通过针对本科生和代表性不足的社区的外展活动，让不同的研究人员群体参与到该项目中。研究人员将制定和研究基本的统计推理任务，例如分布估计、假设检验和分布属性在上述信息约束下进行估计。一个特别感兴趣的方向是共享随机性的可用性对分布式机器学习系统的其他约束的影响。虽然随机性在通信复杂性问题中的作用已经被研究，但它在机器学习系统中的作用经常被忽视。该项目将整合计算机科学、信息论、机器学习和统计学的思想，寻求为这些社区的研究人员架起桥梁。该项目的所有研究结果将通过出版物传播，并将在研究者的网站上公开提供。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。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Robust Estimation for Random Graphs

随机图的鲁棒估计

DOI：
发表时间：
2022-01
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Acharya, Jayadev;Kamath, Gautam;Jain, Ayush;Suresh, Ananda Theertha;Zhang, Huanyu
通讯作者：
Zhang, Huanyu

Sample Complexity of Distinguishing Cause from Effect

区分原因和结果的复杂性示例

DOI：
发表时间：
2023-04
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Acharya, Jayadev;Bhadane, Sourbh;Bhattacharyya, Arnab;Kandasamy, Saravanan;Sun, Ziteng
通讯作者：
Sun, Ziteng

Robust Testing and Estimation under Manipulation Attacks

操纵攻击下的鲁棒测试和估计