CAREER: Learning Algorithms with Robustness and Efficiency Guarantees

从在图像分类中取得超乎人类的表现，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。为了充分挖掘机器学习的潜力并扩大其影响范围，计算机科学家试图开发一种全面的理论来解释现有方法的力量和局限性，并提供改进的途径。该项目旨在通过解决限制机器学习更广泛应用的两个紧迫挑战，为这一方向做出贡献：(I)随着时间的推移，通过多个行动和决定来解决更复杂的任务，以及(Ii)从损坏的数据中学习。目标是通过一个统一的框架研究现有方法的性能和潜在的缺点，并开发可证明是健壮和高效的新算法。与其研究目标相一致，该项目结合了威斯康星大学麦迪逊分校本科生和研究生课程的发展、研究生的培训和本科生的研究机会。更详细地，该项目重点关注在噪声数据下学习和从数据中学习组合算法的主题。在第一个主题中，研究人员将基于最近在处理噪声标签方面的进展，将结果扩展到更复杂的环境，如多类分类，并将设计在许多实际情况下表现良好的更有效的方法。在第二个主题中，研究人员将研究如何使用机器学习，通过连接数据驱动算法设计和强化学习领域，自动开发适合特定应用的高效算法。这两个主题的重点将是拟议方法的统计和计算复杂性。该项目有可能通过学习提供一种新的算法设计范例，并使机器学习系统健壮，从而支持涉及大量噪声的新应用领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.