RI: Small: Training Modularized Learning Systems

RI：小型：训练模块化学习系统

• 批准号: 1910077
• 负责人: Jacob Abernethy
• 金额: $ 44.97万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910077&HistoricalAwards=false
• 关键词: RI; Small; Training; Modularized; Learning

Machine Learning systems are becoming ubiquitous and increasingly complex in modern life. Many devices such as mobile phones continually run dozens of predictive models, and these models receive input not only from the user but also from each other. One way to think about the unexpected challenges of multiple interacting learning systems is to consider how humans interact in personal relationships or even how governments engage with each other during international disputes. Such scenarios involve hard-to-predict dynamics, where the introduction of a small amount of information or minor changes to strategy can give rise to highly different outcomes. This project aims to understand these interacting dynamics from an algorithmic perspective, with an eye towards designing modular learning systems where the implementer can be certain that the dynamics of training will reach a desired solution. The work will significantly increase the range of tasks and challenges where learning systems are applied in the real world and will have a strong impact on how artificial intelligence interacts with society.The project begins with a focus on game theory and builds off of a number of both classical and recent results in solving so-called min-max problems, where one wants to find the equilibrium of a zero-sum game. The hugely popular Generative Adversarial Networks provide a great example where the training objective is framed as two competing modules engaged in a search for a min-max solution. There has been a great deal of work in finding equilibria using learning systems, and recent work by the investigator has shown that several fundamental convex optimization procedures can be viewed through the lens of learning in repeated play. The award will help support the further development of mathematical frameworks to extend these results beyond convex optimization and to design efficient algorithms with provable guarantees in non-convex settings. One of the areas of particular interest will be the use of continuous-time analysis in training complex multiplayer problems, to understand when such dynamics lead to stable outcomes and when they elicit chaotic behavior.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.

机器学习系统在现代生活中变得越来越普遍和复杂。诸如移动的电话之类的许多设备持续地运行数十个预测模型，并且这些模型不仅从用户而且从彼此接收输入。思考多个交互学习系统所带来的意想不到的挑战的一种方法是考虑人类如何在个人关系中互动，甚至考虑政府如何在国际争端中相互参与。这种情景涉及难以预测的动态，其中引入少量信息或对策略进行微小更改可能会导致截然不同的结果。该项目旨在从算法的角度理解这些相互作用的动态，着眼于设计模块化学习系统，其中实施者可以确定培训的动态将达到所需的解决方案。这项工作将显著增加学习系统在真实的世界中应用的任务和挑战范围，并将对人工智能如何与社会互动产生强烈影响。该项目以博弈论为重点，建立在解决所谓的最小-最大问题的经典和最新结果的基础上，其中人们希望找到零和博弈的均衡。广受欢迎的生成对抗网络提供了一个很好的例子，其中训练目标被构建为两个竞争模块，用于搜索最小-最大解决方案。已经有大量的工作在寻找平衡使用学习系统，最近的工作调查表明，几个基本的凸优化程序可以通过透镜的学习在重复播放。该奖项将有助于支持数学框架的进一步发展，以将这些结果扩展到凸优化之外，并设计出在非凸设置中具有可证明保证的高效算法。特别感兴趣的领域之一将是在训练复杂的多人问题中使用连续时间分析，以了解这种动态何时导致稳定的结果，何时引发混乱的行为。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。