Collaborative Research: AF: Small: New Directions in Algorithmic Replicability

合作研究：AF：小：算法可复制性的新方向

• 批准号: 2342244
• 负责人: Vinodchandran Variyam
• 金额: $ 33.77万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342244&HistoricalAwards=false
• 关键词: Collaborative; Research; AF; Small; New

Replicability and reproducibility in science are critical concerns. The fundamental requirement that scientific results and experiments be reproducible and replicable is central to the development and evolution of science. In recent years, these concerns have been even more amplified as several scientific disciplines are turning to data-driven research that enables exponential progress from data democratization and affordable computing resources. The replicability/reproducibility issue has received attention from a wide spectrum of entities ranging from general media publications to scientific publication venues to professional and scientific bodies. This project focuses on addressing the pivotal need for replicability and reproducibility in scientific research that involves randomized computations.The project will explore new directions in algorithmic replicability. The approach is threefold: (1) defining and formalizing new concepts of replicability across various computational contexts, (2) developing methodologies for the design of replicable algorithms, and (3) investigating the limitations and costs associated with replicability. This initiative seeks to contribute fundamentally to the field of replicable and reproducible computations. It focuses on the development of innovative algorithms and establishing lower bounds in replicability. The research is expected to have a significant impact on areas such as machine learning and large data analysis. Beyond these immediate fields, the principles and techniques devised under this project are anticipated to influence a broader spectrum of scientific and technological domains.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.

科学中的可复制性和可重复性是关键问题。科学结果和实验可再现和可复制的基本要求对于科学的发展和演变至关重要。近年来，随着一些科学学科转向数据驱动的研究，这些问题得到了更加放大，这些研究可以从数据民主化和负担得起的计算资源中取得指数级的进步。从一般媒体出版物到科学出版物场所再到专业和科学机构的广泛实体，可复制性/可重复性问题引起了人们的关注。该项目着重于解决涉及随机计算的科学研究中复制性和可重复性的关键需求。该项目将探索算法可复制性的新方向。该方法是三个方面：（1）定义和形式化各种计算环境中可复制性的新概念，（2）开发用于设计可复制算法的方法，以及（3）研究与可复制性相关的限制和成本。该计划旨在从根本上为可复制和可再现的计算领域做出贡献。它着重于创新算法的发展，并建立了可复制性的下限。预计该研究将对机器学习和大型数据分析等领域产生重大影响。除了这些直接领域之外，该项目下设计的原理和技术还预计会影响更广泛的科学和技术领域。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子的智力优点和更广泛的影响审查标准通过评估来获得支持。