CAREER: Learning Algorithms with Robustness and Efficiency Guarantees

职业：学习具有鲁棒性和效率保证的算法

基本信息

批准号：
2144298
负责人：
Ilias Diakonikolas
金额：
$ 63.98万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2027-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144298&HistoricalAwards=false
关键词：
CAREER Learning Algorithms Robustness Efficiency

项目摘要

From achieving super-human performance in image classification to beating human champions in complex games, machine learning has seen enormous success in recent years. To unravel the full potential of machine learning and extend its reach, computer scientists seek to develop a comprehensive theory that explains the power and limitations of existing methods and offers avenues for improvement. This project aims to contribute in this direction by tackling two pressing challenges that limit the broader applicability of machine learning: (i) solving more complex tasks with multiple actions and decisions over time and (ii) learning from corrupted data. The goal is to study the performance and potential shortcomings of existing approaches through a unified framework and develop novel algorithms that are provably robust and efficient. In tandem with its research goals, the project incorporates the development of undergraduate and graduate courses at UW Madison, the training of graduate students, and research opportunities for undergraduates.In more detail, the project focuses on the themes of learning under noisy data and learning combinatorial algorithms from data.In the first theme, the investigator will build upon recent advances in dealing with noisy labels extending the results to more complex settings like multi-class classification, and will design more efficient methods that perform well in many practical situations. In the second theme, the investigator will study the use of Machine Learning to automatically develop efficient algorithms tailored to a specific application by bridging the areas of data-driven algorithm design and reinforcement learning. The focus in both themes will be on the statistical and computational complexity of the proposed methods. The project has the potential to offer a new paradigm of algorithm design through learning and to robustify machine learning systems enabling new application domains that involve large amounts of noise.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.

从在图像分类中实现超人的表现到在复杂游戏中击败人类冠军，机器学习近年来取得了巨大的成功。为了揭示机器学习的全部潜力并扩大其影响力，计算机科学家试图开发一种综合理论，该理论解释了现有方法的力量和局限性，并提供了改进的途径。该项目旨在通过解决两个紧迫的挑战，以限制机器学习的更广泛的适用性来朝着这个方向做出贡献：（i）随着时间的推移和（ii）从损坏的数据中学习的多个动作和决策，解决了更复杂的任务。目的是通过统一框架研究现有方法的性能和潜在缺点，并开发出可证明有力和有效的新型算法。该项目与其研究的目标同时结合了UW麦迪逊大学的本科和研究生课程的发展，对研究生的培训以及对本科生的研究机会。更详细地，该项目着重于嘈杂的数据和学习中的学习主题和学习组合算法在数据中的最新设置，这些算法将在第一个主题上进行，这些算法将在第一个主题上进行，这些算法与第一个主题相结合，在诸如第一个主题上，构建了诸如研究的成员，在此范围内构建依据。多级分类，并将设计更有效的方法，这些方法在许多实际情况下表现良好。在第二个主题中，调查人员将研究机器学习来自动开发针对特定应用程序量身定制的有效算法，通过桥接数据驱动的算法设计和增强学习。这两个主题的重点都将放在所提出方法的统计和计算复杂性上。该项目有潜力通过学习提供新的算法设计范式，并强大的机器学习系统实现了涉及大量噪音的新应用领域。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。