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AitF: FULL: From Worst-Case to Realistic-Case Analysis for Large Scale Machine Learning Algorithms

AitF：完整：大规模机器学习算法从最坏情况到现实情况分析

基本信息

批准号：
1535967
负责人：
Maria-Florina Balcan
金额：
$ 72万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535967&HistoricalAwards=false
关键词：
AitF FULL Worst Case Realistic

项目摘要

The aim of this project is to develop mathematical models, analysis, and algorithms that will advance both the design and understanding of large-scale machine learning systems. In recent years, machine learning has come into widespread use across a range of applications, and we have also seen significant advances in the theoretical understanding of learning processes. Yet despite these successes, there remains a gulf between theory and application. For example, applications often demonstrate success on problems that theory tells us are intractable in the worst case. Furthermore, as modern machine learning applications scale up from learning of single tasks to learning many tasks simultaneously, new theory is needed to analyze these larger scale multi-task learning settings. This project aims to bridge this gap by developing and applying theory targeted toward realistic-case analysis of learning problems, which capture the structures that enable applications to succeed even when theoretical analyses show the impossibility of doing so in the worst case. This work will be guided by problems at the core of NELL and InMind, two current learning systems that address large-scale multi-task machine learning problems, for reading the web and providing highly personalized electronic assistants to hundreds of interconnected mobile phone users.More specifically, this project has three main components:(1) To develop computationally efficient algorithms for clustering, constrained optimization, and related optimization tasks crucial to large-scale machine learning, with provable guarantees under natural, realistic non-worst-case analysis models.(2) To develop foundations and practical algorithms for multi-task and life-long learning that exploit explicit and implicit structure to minimize key resources including computation time and human labeling effort, as well as address key constraints such as privacy.(3) To apply the algorithms developed to solve key challenges in two current large-scale learning systems, NELL and InMind.The proposed work will aid the development of large-scale machine learning applications, as well as create important connections between multiple areas of significant importance in modern machine learning and theoretical computer science. In addition to advising students on topics connected to this project, research progress (on multi-task learning, life-long learning, and clustering) will be integrated in the curricula of several courses at CMU and course materials will be made available on the world wide web. Course projects based on this research will be available to students in the introductory machine learning course at CMU, which enrolls over 600 students each year. In addition, students seeking topics for undergraduate thesis or independent study may also pursue research affiliated with this project.

该项目的目的是开发数学模型，分析和算法，以促进大型机器学习系统的设计和理解。近年来，机器学习在一系列应用中得到了广泛的应用，我们也看到了学习过程的理论理解的重大进展。然而，尽管取得了这些成功，理论和应用之间仍然存在鸿沟。例如，应用程序通常在理论告诉我们在最坏情况下难以解决的问题上取得成功。此外，随着现代机器学习应用从单个任务的学习扩展到同时学习多个任务，需要新的理论来分析这些更大规模的多任务学习设置。该项目旨在通过开发和应用针对学习问题的现实案例分析的理论来弥合这一差距，该理论捕捉了即使在理论分析显示在最坏情况下不可能这样做的情况下也能使应用程序成功的结构。这项工作将以NELL和InMind的核心问题为指导，这两个当前的学习系统解决了大规模多任务机器学习问题，用于阅读网络，并为数百个互联的移动的电话用户提供高度个性化的电子助理。更具体地说，这个项目有三个主要组成部分：（1）开发用于集群、约束优化和对大规模机器学习至关重要的相关优化任务的计算高效算法，在自然的、现实的非最坏情况分析模型下具有可证明的保证。(2)开发多任务和终身学习的基础和实用算法，利用显式和隐式结构来最大限度地减少关键资源，包括计算时间和人工标记工作，以及解决隐私等关键约束。(3)应用开发的算法来解决当前两个大规模学习系统NELL和InMind中的关键挑战。拟议的工作将有助于大规模机器学习应用程序的开发，并在现代机器学习和理论计算机科学中的多个重要领域之间建立重要联系。除了就与该项目有关的主题向学生提供咨询外，研究进展（关于多任务学习、终身学习和集群）将被纳入CMU的几门课程的课程中，课程材料将在万维网上提供。基于这项研究的课程项目将提供给CMU机器学习入门课程的学生，该课程每年招收600多名学生。此外，寻求本科论文或独立研究主题的学生也可以从事与本项目相关的研究。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems

DOI：
10.5555/3237383.3237446
发表时间：
2018-07
期刊：
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
Maria-Florina Balcan;Avrim Blum;Shang-Tse Chen
通讯作者：
Maria-Florina Balcan;Avrim Blum;Shang-Tse Chen

Semi-bandit Optimization in the Dispersed Setting

分散环境中的半老虎优化