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无处不在，越来越复杂。许多设备（例如移动电话）不断运行数十种预测模型，这些模型不仅从用户那里接收到彼此的输入。考虑多个互动学习系统的意外挑战的一种方法是考虑人类如何在人际关系中互动，甚至在国际争端期间如何相互互动。这样的方案涉及难以预测的动态，其中引入少量信息或对策略的少量更改可能会导致高度不同的结果。该项目旨在从算法的角度了解这些相互作用的动态，并着眼于设计模块化学习系统，在该系统中，实施者可以确定训练的动态将达到所需的解决方案。这项工作将显着增加在现实世界中应用学习系统的任务和挑战范围，并将对人工智能与社会的相互作用产生重大影响。该项目始于重点是游戏理论，并建立了许多古典和最近的结果，从而解决了所谓的Min-Max问题，希望在其中找到一个零售游戏的平衡。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，从事搜索Min-Max解决方案。在使用学习系统的情况下，在寻找平衡方面已经做了很多工作，研究人员最近的工作表明，可以通过重复游戏中的学习镜头来查看几种基本的凸优化程序。该奖项将有助于支持数学框架的进一步开发，以将这些结果扩展到凸优化之外，并在非convex设置中具有可证明保证的有效算法。特别感兴趣的领域之一将是在培训复杂的多人游戏问题中使用连续的时间分析，以了解这种动态何时导致稳定的结果以及它们引起混乱的行为。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和更广泛影响的评估审查审查标准来通过评估来通过评估来获得支持的。