Tighter error bounds for representation learning and lifelong learning

表征学习和终身学习的更严格的误差范围

• 批准号: RGPIN-2018-03942
• 负责人: Mehta, Nishant
• 金额: $ 2.84万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=749867
• 关键词: Tighter; error; bounds; representation; learning

The success of machine learning algorithms crucially hinges on which numerical features are used to represent data. An arguably large part of the success of human learning is that we not only learn representations to perform well on prediction tasks, but we also reuse these representations to more efficiently learn newly encountered but similar tasks. We are now in an era where "deep" machine learning methods actually learn useful representations of data automatically. These deep learning methods are becoming vital to many other fields: just a few successful applications include drug design, 3D rendering for computer graphics, and beating top-level human players in games such as Go. Despite these great empirical successes, our mathematical understanding for why these methods work well and how to best train them is lacking. Part I of my research is to develop new theory to understand how well a deep model will perform when it makes predictions about new data. My strategy to obtain better performance guarantees is to use theory that leverages specific properties of the actual data an algorithm sees and specific properties of the algorithm itself. By considering both of these important aspects of training, I expect to achieve success for deep learning models as well. Another important part of my analysis is to answer the following question: If a deep learning model is insensitive to certain types of transformations of its input, can it successfully be trained using less data as a result?One of the greatest advancements the field of machine learning can make is to shift from learning each new thing in isolation to reusing what has been learned in the past when learning new tasks. This continual transfer when learning an endless sequence of tasks is known as lifelong learning. While some research has begun in this important area, the mathematical theory for how well algorithms can perform lifelong learning is lacking, especially with regards to adaptive algorithms that transfer much more from their past experiences when they encounter tasks that are highly similar to past tasks. Part II of my research is to design algorithms for lifelong learning, including developing mathematical guarantees for these algorithms. I aim to answer questions like the following: If a learning agent encounters a series of slowly changing learning tasks, can we pool the data from previous tasks in order to learn new tasks using much less data? My strategy will be to adapt powerful ideas from sequential prediction, a vital yet previously untapped resource for work in lifelong learning.Both parts of this work can fundamentally advance the field of machine learning, which already is revolutionizing a number of sciences and industries. The students participating in this research will be excellently-equipped to fuel Canada's technological contributions at the global stage.

机器学习算法的成功至关重要地取决于使用数值特征来表示数据的成功。可以说，人类学习成功的很大一部分是，我们不仅学习表达在预测任务上表现良好，而且我们还将这些表示形式重复使用，以更有效地学习新遇到的新遇到的，而且是类似的任务。现在，我们正处于一个“深”机器学习方法实际上自动学习有用的数据表示的时代。这些深度学习方法对许多其他领域变得至关重要：只有一些成功的应用程序包括药物设计，用于计算机图形技术的3D渲染以及在GO等游戏中击败顶级人类玩家。尽管取得了巨大的经验成功，但我们对这些方法为什么效果很好以及如何最好地训练它们的数学理解是缺乏的。我的研究的第一部分是开发新理论，以了解一个深层模型对新数据的预测时的表现。我获得更好的性能保证的策略是使用理论，即利用算法看到的实际数据的特定属性和算法本身的特定属性。通过考虑培训的这两个重要方面，我也希望也能为深度学习模型取得成功。我的分析的另一个重要部分是回答以下问题：如果深度学习模型对其输入的某些类型的转换不敏感，可以成功地使用较少的数据对其进行培训？在学习无尽的任务序列时，这种持续的转移称为终身学习。尽管一些研究已经开始在这一重要领域，但缺乏算法能够执行终身学习的数学理论，尤其是在自适应算法方面，当他们遇到与过去任务高度相似的任务时，这些算法从过去的经历中转移了更多。我的研究的第二部分是设计用于终身学习的算法，包括为这些算法开发数学保证。我的目标是回答以下问题：如果学习代理遇到一系列缓慢改变的学习任务，我们是否可以从先前任务中汇总数据以使用更少的数据来学习新任务？我的策略将是从顺序预测中调整有力的想法，这是一个重要但以前未开发的用于终身学习的资源。这项工作的部分都可以从根本上推进机器学习领域，而机器学习领域已经在彻底改变了许多科学和行业。参与这项研究的学生将有足够的能力在全球阶段为加拿大的技术贡献促进。