CAREER: Foundations of Scalable Nonconvex Min-Max Optimization

职业生涯：可扩展非凸最小-最大优化的基础

• 批准号: 2144985
• 负责人: Meisam Razaviyayn
• 金额: $ 56.22万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144985&HistoricalAwards=false
• 关键词: CAREER; Foundations; Scalable; Nonconvex; Min

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Recent advances in the fields of Machine Learning and Data Science have been profoundly influenced by the development of powerful computational tools and efficient algorithms. However, training the latest Machine Learning models continuously necessitates that new algorithms and techniques be developed to solve increasingly complex problems at much larger scales. This project is concerned with particular classes of min-max optimization problems as they arise in many important applications of modern Data Science, e.g, training fair ML models that are not biased against individuals with certain sensitive attributes, designing AI systems that reliably perform against changes in the input data, and training ML models for generating artificial music. The research agenda focuses on developing new algorithms to solve various computational issues associated with such min-max problems; it will provide a natural vehicle to create educational content, and foster mentoring opportunities for undergraduate and graduate students. A central component is outreach to high school students via the USC Neighborhood Academic Initiative (NAI) and the USC Viterbi K-12 STEM Center; these programs serve K-12 schools and teachers in Southern California that face systemic inequities. The main technical aim is to develop both theoretical foundations and scalable algorithms for (stochastic) non-convex min-max optimization problems. The efforts will address several longstanding open questions related to the robust operation of these non-convex models. Special attention will be given to designing provably efficient algorithms for computing first-order stationary solutions of certain structured non-convex (stochastic) min-max problems for which currently no algorithm with polynomial iteration complexity is known to exist. The envisioned algorithms exploit the structure of the objective function and of the constraint sets, leverage recent advances in the field of numerical differentiation, and explore tradeoffs in memory and processing capabilities offered by computational platforms. The fundamental minimum computational efforts required for finding stationary solutions will be studied under different scenarios motivated by a wide range of applications such as robust machine learning, fair statistical inference, and training generative models, and the research outcomes are expected to have an impact on these applications.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。机器学习和数据科学领域的最新进展受到强大的计算工具和有效算法的开发的深刻影响。但是，培训最新的机器学习模型不断需要开发新的算法和技术来解决更大范围的日益复杂的问题。该项目涉及特定类别的Min-Max优化问题，因为它们在现代数据科学的许多重要应用中出现，例如，培训公平的ML模型，这些模型不会偏向具有某些敏感属性的个体，设计了AI系统，这些系统可以可靠地针对输入数据的变化以及培训ML模型来可靠地执行生成人造音乐。研究议程着重于开发新算法，以解决与此类Min-Max问题相关的各种计算问题。它将提供一种自然的工具来创建教育内容，并为本科生和研究生培养指导机会。通过USC邻里学术计划（NAI）和USC Viterbi K-12 STEM中心，与高中生的核心组成部分是向高中生推广。这些计划为面临系统不平等的南加州的K-12学校和教师提供服务。主要的技术目的是为（随机）非凸线最低最大优化问题开发理论基础和可扩展算法。这些努力将解决与这些非凸模型的强大操作有关的几个长期开放问题。将特别注意设计有效的有效算法，以计算某些结构性非凸（随机）最小值问题的一阶固定解决方案，该算法目前尚无具有多项式迭代复杂性的算法。设想的算法利用了目标函数的结构和约束集的结构，利用数值差异领域的最新进展，并探索计算平台提供的内存和处理能力方面的权衡。在不同的情况下，将研究寻找固定解决方案所需的基本最低计算工作，这些方案的促进，诸如强大的机器学习，公平的统计推理和培训生成模型等广泛的应用程序，预计将对这些应用产生影响。该奖项将反映出NSF的法定任务，反映了通过评估的范围来审查构成群体的范围，并构成了构成群体的支持。

项目成果

A Stochastic Optimization Framework for Fair Risk Minimization

公平风险最小化的随机优化框架

• 发表时间: 2022
• 期刊: Transactions on machine learning research
• 作者: Lowy, Andrew;Baharlouei, Sina;Pavan, Rakesh;Razaviyayn, Meisam;Beirami, Ahmad
• 通讯作者: Beirami, Ahmad

Stochastic Differentially Private and Fair Learning

• DOI: 10.48550/arxiv.2210.08781
• 发表时间: 2022-10
• 作者: Andrew Lowy;Devansh Gupta;Meisam Razaviyayn

Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes

• DOI: 10.48550/arxiv.2210.14410
• 发表时间: 2022-10
• 作者: Sina Baharlouei;Fatemeh Sheikholeslami;Meisam Razaviyayn;Zico Kolter