Conference: UCLA Synthetic Data Workshop

会议：加州大学洛杉矶分校综合数据研讨会

• 批准号: 2309349
• 负责人: Guang Cheng
• 金额: $ 1.5万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309349&HistoricalAwards=false
• 关键词: Conference; UCLA; Synthetic; Data; Workshop; Conference; UCLA; Synthetic; Data; Workshop

This award supports participation by experts from diverse mathematical disciplines and computer science, especially graduate students and other early-career researchers, in the upcoming UCLA Synthetic Data Workshop to be held at the University of California, Los Angeles, from April 13 to April 14, 2023. The goal of the workshop is to foster the collaboration of researchers in several areas connected to synthetic data and data privacy, including differential privacy, fairness, and adversarial robustness. The rationale for this activity is that synthetic data generation is a rapidly growing and highly disciplinary research area that draws much attention. For the development of algorithmic procedures for fraud deception and spam identification, as well as for the construction of AI-driven models in manufacturing and supply chain management, synthetic data has become a valuable resource. The goal of this workshop is to investigate scientific foundations that are spawned by these advancements and examine new strategies for solving open problems. The workshop will also have a substantial pedagogical component in the form of introductory talks that will cover background and recent exciting progress in its focus areas. These talks will be accessible to non-experts, including graduate students and junior researchers.Synthetic data is especially useful when obtaining real-world data is either too costly or too risky. Recent results hint at a new and promising direction that practitioners may effectively train AI models by addressing edge scenarios and dangerous occurrences while using synthetic data. Despite numerous successful applications of synthetic data, its scientific foundation, e.g., the tradeoff among fidelity, utility, and privacy, is still missing. In addition, industrial standards for generating and utilizing synthetic data, as well as the privacy law concerning synthetic data, are yet to be established. This workshop will provide an environment for experts to exchange their ideas for open questions about synthetic data, such as whether or not privacy is lost when creating synthetic data, whether or not using synthetic data affects fairness, and how, at the most basic level, one should judge the quality and usefulness of synthetic data. The website for the workshop is https://ucla-synthetic-data.github.io/.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.

该奖项支持来自多元化数学学科和计算机科学的专家，尤其是研究生和其他早期研究人员的参与，即即将举行的UCLA合成数据研讨会，将于4月13日至2023年4月14日在加利福尼亚大学的洛杉矶大学在洛杉矶大学举行。对抗性的鲁棒性。这项活动的理由是，合成数据生成是一个快速增长且高度纪律的研究领域，引起了很多关注。为了制定用于欺诈欺诈和垃圾邮件识别的算法程序，以及在制造和供应链管理中构建AI驱动模型的构建，合成数据已成为宝贵的资源。该研讨会的目的是研究这些进步所产生的科学基础，并研究解决开放问题的新策略。该研讨会还将以介绍性演讲的形式拥有实质性的教学成分，该演讲将涵盖其重点领域的背景和令人兴奋的进步。非专家（包括研究生和初级研究人员）将可以访问这些谈判。在获得现实世界数据时，合成数据尤其有用。最近的结果暗示了一个新的有前途的方向，从业人员可以通过解决综合数据时解决边缘方案和危险事件来有效地训练AI模型。尽管合成数据的许多成功应用，但其科学基础，例如，富裕，公用事业和隐私之间的权衡仍然缺失。此外，尚待建立用于生成和利用合成数据的工业标准以及有关合成数据的隐私法。该研讨会将为专家提供一个环境，以将其思想交换为有关综合数据的开放问题，例如在创建合成数据时是否丢失了隐私，是否使用合成数据会影响公平性，以及在最基本的水平上，应该判断合成数据的质量和实用性。研讨会的网站是https://ucla-synthetic-data.github.io/.Io/.This Award反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准通过评估来获得支持的。