Collaborative Research: Novel Computational and Statistical Approaches to Prediction and Estimation
协作研究:预测和估计的新颖计算和统计方法
基本信息
- 批准号:1841187
- 负责人:
- 金额:$ 4.83万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-06-01 至 2018-11-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Terabytes of data are collected by companies and individuals every day. These data possess no value unless one can efficiently process them and use them to make decisions. The scale and the streaming nature of data pose both computational and statistical challenges. The objective of this research project is to develop novel approaches to making online, real-time decisions when data are constantly evolving and highly structured. In particular, this project focuses on online prediction problems involving multiple users in dynamic networks. The project also aims to tackle the privacy issues arising in such multi-user scenarios.In recent years, it was shown that a majority of online machine learning algorithms can be viewed as solutions to approximate dynamic programming (ADP) problems that incorporate one additional datum per step. Along with directly addressing the computational concerns, the ADP framework also provides guaranteed performance on prediction problems. This project is to use and extend the ADP framework to develop prediction algorithms that simultaneously address the issues of computation, robustness, non-stationarity, privacy, and multiplicity of users.
公司和个人每天收集的数据达tb。除非人们能够有效地处理这些数据并利用它们做出决策,否则这些数据没有任何价值。数据的规模和流性质对计算和统计都提出了挑战。这个研究项目的目标是在数据不断演变和高度结构化的情况下,开发新的方法来做出在线、实时的决策。本项目特别关注动态网络中涉及多用户的在线预测问题。该项目还旨在解决这种多用户场景中出现的隐私问题。近年来,研究表明,大多数在线机器学习算法可以被视为近似动态规划(ADP)问题的解决方案,其中每一步包含一个额外的数据。除了直接解决计算问题外,ADP框架还在预测问题上提供了保证的性能。该项目将使用和扩展ADP框架来开发预测算法,同时解决计算、鲁棒性、非平稳性、隐私性和用户多样性等问题。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Alexander Rakhlin其他文献
Alexander Rakhlin的其他文献
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{{ truncateString('Alexander Rakhlin', 18)}}的其他基金
Interpolation Methods in Statistics and Machine Learning
统计和机器学习中的插值方法
- 批准号:
1953181 - 财政年份:2020
- 资助金额:
$ 4.83万 - 项目类别:
Continuing Grant
Collaborative Research: Novel Computational and Statistical Approaches to Prediction and Estimation
协作研究:预测和估计的新颖计算和统计方法
- 批准号:
1521529 - 财政年份:2015
- 资助金额:
$ 4.83万 - 项目类别:
Continuing Grant
Participant Support for attendants to the program Mathematics of Machine Learning (Barcelona)
为参加机器学习数学课程(巴塞罗那)的参与者提供支持
- 批准号:
1342739 - 财政年份:2013
- 资助金额:
$ 4.83万 - 项目类别:
Standard Grant
AF: Small: From Statistical to Worst-Case Learning: A Unified Framework
AF:小:从统计到最坏情况学习:统一框架
- 批准号:
1116928 - 财政年份:2011
- 资助金额:
$ 4.83万 - 项目类别:
Standard Grant
CAREER: Statistical and Computational Complexities of Modern Learning Problems
职业:现代学习问题的统计和计算复杂性
- 批准号:
0954737 - 财政年份:2010
- 资助金额:
$ 4.83万 - 项目类别:
Continuing Grant
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