TRIPODS: Institute for Foundations of Data Science

TRIPODS：数据科学研究所

• 批准号: 2023166
• 负责人: Maryam Fazel
• 金额: $ 485.3万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023166&HistoricalAwards=false
• 关键词: TRIPODS; Institute; Foundations; Data; Science

Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these. Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, organized around the goal of tackling these critical issues. Members of IFDS have complementary strengths in the TRIPODS disciplines of mathematics, statistics, and theoretical computer science, and a proven record of collaborating to push theoretical boundaries by synthesizing knowledge and experience from diverse areas. Students and postdoctoral members of IFDS will be trained to be fluent in the languages of several disciplines, and able to bridge these communities and perform transdisciplinary research in the foundations of data science. In concert with its research agenda, IFDS will engage the data science community through workshops, summer schools, and hackathons. Its diverse leadership, committed to equity and inclusion, proposes extensive plans for outreach to traditionally underrepresented groups. Governance, management, and evaluation of the institute will build on the successful and efficient models developed during Phase I.To address critical issues at the cutting edge of data science research, IFDS will organize its research around four core themes. The complexity theme will synthesize various notions of complexity from multiple disciplines to make breakthroughs in the analysis of optimization and sampling methods, develop tools for assessing the complexity of data models, and seek new methods with better complexity properties, to make complexity a more powerful tool for understanding and inventing algorithms in data science. The robustness theme considers data that contains errors or outliers, possibly due to an adversary, and will design methods for data analysis and prediction that are robust in the face of these errors. The theme on closed-loop data science tackles the issues of acquiring data in ways that reveal the information content of the data efficiently, using strategic and sequential policies that leverage information gathered already from past data. The theme on ethics and algorithms addresses issues of fairness and bias in machine learning, data privacy, and causality and interpretability. The four themes intersect in many ways, and most IFDS researchers will work in two or more of them. By making concerted progress on these fundamental fronts, IFDS will lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. Additionally, IFDS will organize and host activities that engage the data science community at all levels of seniority. Annual workshops will focus on the critical issues identified above and others that are sure to arise over the next five years. Comprehensive plans for outreach and education will draw on previous experience of the Phase I institutes and leverage institutional resources at the four sites. Collaborations with domain science researchers in academia, national laboratories, and industry, so important in illuminating issues in the fundamentals of data science, will continue through the many channels available to IFDS members, including those established in the TRIPODS+X program. Relationships with other institutes at each IFDS site will further extend the impact of IFDS on domain sciences and 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.

数据科学正在对科学和社会产生巨大的影响，但它的成功也揭示了阻碍进一步发展的紧迫的新挑战。许多机器学习过程产生的结果和决策对数据中的错误和腐败并不鲁棒;随着对数据隐私的担忧不断增加，数据科学算法正在产生有偏见和不公平的结果;适合动态交互环境的机器学习系统的开发程度低于静态问题的相应工具。只有通过呼吁数据科学的基础，我们才能理解和应对这些挑战。在三个TRIPODS第一阶段研究所工作的基础上，新的数据科学基础研究所（IFDS）汇集了来自华盛顿、威斯康星-麦迪逊、加利福尼亚-圣克鲁斯和芝加哥大学的研究人员，围绕解决这些关键问题的目标组织起来。IFDS的成员在TRIPODS的数学、统计学和理论计算机科学学科方面具有互补优势，并通过综合不同领域的知识和经验，在合作推动理论边界方面有着良好的记录。IFDS的学生和博士后成员将接受培训，精通多个学科的语言，能够在这些社区之间架起桥梁，并在数据科学的基础上进行跨学科研究。与其研究议程相一致，IFDS将通过研讨会，暑期学校和黑客松参与数据科学界。其多元领导层致力于公平和包容，提出了广泛的计划，以拓展传统上代表性不足的群体。该研究所的治理，管理和评估将建立在第一阶段开发的成功和有效的模型基础上，以解决数据科学研究前沿的关键问题，IFDS将围绕四个核心主题组织其研究。复杂性主题将综合多个学科的各种复杂性概念，在优化和采样方法分析方面取得突破，开发评估数据模型复杂性的工具，并寻求具有更好复杂性特性的新方法，使复杂性成为理解和发明数据科学算法的更强大工具。鲁棒性主题考虑可能由于对手而包含错误或离群值的数据，并将设计面对这些错误的数据分析和预测方法。关于闭环数据科学的主题解决了以有效揭示数据信息内容的方式获取数据的问题，使用战略和顺序政策，利用从过去数据中收集的信息。关于道德和算法的主题涉及机器学习中的公平性和偏见，数据隐私以及因果关系和可解释性。这四个主题在许多方面相互交叉，大多数IFDS研究人员将在其中两个或更多的工作。通过在这些基本方面取得协调一致的进展，IFDS将降低一些障碍，以更好地理解数据科学方法，提高其有效性并与应用领域更广泛的相关性。此外，IFDS还将组织和举办各种活动，让数据科学界的各个级别都参与进来。年度讲习班将侧重于上文确定的关键问题和今后五年肯定会出现的其他问题。全面的外联和教育计划将借鉴第一阶段研究所以往的经验，并利用四个地点的机构资源。与学术界、国家实验室和工业界的领域科学研究人员的合作，对于阐明数据科学基础问题至关重要，将通过IFDS成员可用的许多渠道继续进行，包括TRIPODS+X计划中建立的渠道。与IFDS各站点的其他机构的关系将进一步扩大IFDS对领域科学和应用的影响。该奖项反映了NSF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。

项目成果

Towards Sample-efficient Overparameterized Meta-learning

迈向样本高效的过度参数化元学习

• 发表时间: 2021
• 期刊: Advances in neural information processing systems
• 作者: Sun, Yue;Narang, Adhyyan;Gulluk, Ibrahim;Oymak, Samet;Fazel, Maryam
• 通讯作者: Fazel, Maryam

On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds

关于具有性能界限的线性二次高斯控制中的控制器简化

• 发表时间: 2023
• 期刊: Proceedings of Machine Learning Research
• 作者: Ren, Zhaolin;Zheng, Yang;Fazel, Maryam;Li, Na
• 通讯作者: Li, Na

Minimum cost flows, MDPs, and l1-regression in nearly linear time for dense instances

密集实例的近线性时间内的最小成本流、MDP 和 l1 回归

• DOI: 10.1145/3406325.3451108
• 发表时间: 2021
• 期刊: 53rd Annual ACM SIGACT Symposium on Theory of Computing
• 作者: van den Brand, Jan;Lee, Yin Tat;Liu, Yang P.;Saranurak, Thatchaphol;Sidford, Aaron;Song, Zhao;Wang, Di
• 通讯作者: Wang, Di

Structured Logconcave Sampling with a Restricted Gaussian Oracle

• 发表时间: 2020-10
• 期刊: ArXiv
• 作者: Y. Lee;Ruoqi Shen;Kevin Tian

Stochastic Optimization under Distributional Drift

• 发表时间: 2021-08
• 期刊: J. Mach. Learn. Res.
• 作者: Joshua Cutler;D. Drusvyatskiy;Zaïd Harchaoui