Conference: Inaugural CAMDA Conference

最近，随着诸如Chat-GPT和Bard等高度通用的大型语言模型的发布，机器学习再次引起了人们的关注。随着人们对技术进步的社会和经济影响的担忧加剧，许多最成功的机器学习工具仍然很难被理解，它们的内部工作原理甚至连它们的创造者也不清楚。我们认为，一个健康和持久的社会必须建立在易于理解、负责任和可解释的原则之上。本次会议致力于机器学习的数学基础及其在分析可处理环境中的应用，旨在构建工具以更好地理解强大但难以理解的新兴工具，并讨论可解释的替代方法。德克萨斯农工大学是近似理论的历史据点，而近似理论本身就是学习理论的基础。事实上，“一般来说，一个函数能被近似得多好？”的问题先于“一个函数能多好地从点值中近似出来？”本次会议（https://sites.google.com/tamu.edu/camda-conference/）的目标是将严格的数学分析置于数据科学未来发展的中心，以指导对社会和环境负责的进步。来自数学系、电子与计算机工程系和计算机科学系的四位全体演讲人将在这次活动中向跨学科的听众发表演讲。计划举办一个“开放问题”会议，讨论和传播这方面的重要开放问题，其具体目标是吸引初级研究人员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning has recently attracted renewed attention following the release of highly versatile large language models such as Chat-GPT and Bard. As concerns about the social and economic impact of technical advances mount, many of machine learning's most successful tools remain poorly understood and their inner working obscure even to their creators. We believe that a healthy and durable society must be built on well-understood, responsible, and interpretable principles. This conference is dedicated to the mathematical foundations of machine learning and its application in analytically tractable settings with the aim of building the tools to better understand the powerful, but inscrutable emerging tools and to discuss interpretable alternative approaches.Texas A&M University is a historical stronghold of approximation theory, which itself underlies learning theory. Indeed, the question 'how well can a function be approximated in general?' arguably precedes the question 'how well can a function be approximated from point values?'. It is an objective of this conference (https://sites.google.com/tamu.edu/camda-conference/) to place rigorous mathematical analysis at the center of future developments in data science in order to guide socially and environmentally responsible progress. Four plenary speakers from Departments of Mathematics, Electrical and Computer Engineering and Computer Science will address an interdisciplinary audience in this effort. An 'open problems' session is planned for the discussion and dissemination of important open problems in this effort, with the specific goal of attracting junior researchers.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.