Computational Learning Theory

计算学习理论

• 批准号: CRC-2021-00280
• 负责人: Zilles, Sandra
• 金额: $ 14.57万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750770
• 关键词: Computational; Learning; Theory

Machine learning often requires large amounts of potentially expensive data, a problem that is addressed by research on interactive learning. While classical machine learning models assume either randomly selected data or extremely inadequate data, interactive models assume carefully selected data, as is reasonable in a variety of applications. Dr. Zilles' objective is to design and analyze formal models of interactive learning and to develop data-economical algorithms that can efficiently solve complex learning problems. The models and algorithmic techniques that Dr. Zilles and her research team provide may change the way machine learning is deployed. She is aiming at theoretical guarantees, but her long-term goal is to achieve improvements in applied machine learning as well.

机器学习通常需要大量潜在昂贵的数据，这是交互式学习研究解决的问题。虽然经典的机器学习模型假设随机选择的数据或极不充分的数据，但交互式模型假设精心选择的数据，这在各种应用中都是合理的。Zilles博士的目标是设计和分析交互式学习的正式模型，并开发能够有效解决复杂学习问题的数据经济算法。Zilles博士和她的研究团队提供的模型和算法技术可能会改变机器学习的部署方式。她的目标是理论上的保证，但她的长期目标是实现应用机器学习的改进。