Conference: Inaugural CAMDA Conference

最近，随着高度通用的大型语言模型如Chat-GPT和BARD的发布，机器学习重新引起了人们的关注。随着人们对技术进步的社会和经济影响的担忧与日俱增，许多机器学习最成功的工具仍然鲜为人知，它们的内部工作原理甚至对它们的创造者来说也是模糊不清的。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易于分析的环境中的应用，目的是构建工具，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论的历史堡垒，它本身就是学习理论的基础。实际上，一个问题就是‘一个函数的总体逼近能力有多强？’可以说，这个问题出现在“从点值逼近函数的精度有多高？”这个问题之前。(https://sites.google.com/tamu.edu/camda-conference/)会议的目标之一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、电气和计算机工程以及计算机科学系的四位全体演讲者将在这项工作中向跨学科的听众发表讲话。为了讨论和传播这项工作中重要的公开问题，计划举行一个“开放问题”会议，具体目标是吸引初级研究人员。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.