Conference: UCLA Synthetic Data Workshop
会议:加州大学洛杉矶分校综合数据研讨会
基本信息
- 批准号:2309349
- 负责人:
- 金额:$ 1.5万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
该奖项支持来自不同数学学科和计算机科学的专家,特别是研究生和其他早期职业研究人员参加即将于2023年4月13日至4月14日在加州大学洛杉矶分校举行的加州大学洛杉矶分校综合数据研讨会。研讨会的目标是促进研究人员在与合成数据和数据隐私相关的几个领域的合作,包括差异隐私、公平和对抗性鲁棒性。这项活动的基本原理是,合成数据生成是一个迅速发展和高度学科化的研究领域,引起了许多注意。对于开发欺诈欺骗和垃圾邮件识别的算法程序,以及在制造业和供应链管理中构建人工智能驱动的模型,合成数据已成为一种宝贵的资源。本次研讨会的目标是研究由这些进步产生的科学基础,并研究解决开放性问题的新策略。讲习班还将以介绍性会谈的形式提供大量教学内容,介绍其重点领域的背景和最近令人兴奋的进展。这些讲座将对非专家开放,包括研究生和初级研究人员。当获取真实数据的成本太高或风险太大时,合成数据特别有用。最近的结果暗示了一个新的和有希望的方向,即从业者可以通过使用合成数据解决边缘场景和危险事件来有效地训练人工智能模型。尽管合成数据有许多成功的应用,但它的科学基础,例如,在保真度、实用性和隐私性之间的权衡,仍然缺失。此外,合成数据的产生和利用的行业标准和有关合成数据的隐私法尚未制定。本次研讨会将为专家们提供一个环境,就合成数据的开放性问题交换意见,例如在创建合成数据时是否会丢失隐私,使用合成数据是否会影响公平性,以及如何在最基本的层面上判断合成数据的质量和有用性。研讨会的网站是https://ucla-synthetic-data.github.io/.This,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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Guang Cheng其他文献
PDA-cross-linked beta-cyclodextrin: a novel adsorbent for the removal of BPA and cationic dyes.
PDA 交联 β-环糊精:一种用于去除 BPA 和阳离子染料的新型吸附剂。
- DOI:
10.2166/wst.2020.286 - 发表时间:
2020-06 - 期刊:
- 影响因子:2.7
- 作者:
Jianyu Wang;Guang Cheng;Jian Lu;Huafeng Chen;Yanbo Zhou - 通讯作者:
Yanbo Zhou
RBAS: A Real-Time User Behavior Analysis System for Internet TV in Cloud Computing
RBAS:云计算下的互联网电视实时用户行为分析系统
- DOI:
10.1145/2935663.2935664 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
C. Zhu;Guang Cheng;Xiaojun Guo;Yuxiang Wang - 通讯作者:
Yuxiang Wang
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities
BadGD:一个以数据为中心的统一框架,用于识别梯度下降漏洞
- DOI:
10.48550/arxiv.2405.15979 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
ChiHua Wang;Guang Cheng - 通讯作者:
Guang Cheng
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing
TimeAutoDiff:结合自动编码器和扩散模型进行时间序列表格数据合成
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Namjoon Suh;Yuning Yang;Din;Qitong Luan;Shirong Xu;Shixiang Zhu;Guang Cheng - 通讯作者:
Guang Cheng
HIGHER ORDER SEMIPARAMETRIC FREQUENTIST INFERENCE WITH THE PROFILE SAMPLER
使用配置文件采样器进行高阶半参数频率推理
- DOI:
- 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
Guang Cheng;M. Kosorok - 通讯作者:
M. Kosorok
Guang Cheng的其他文献
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{{ truncateString('Guang Cheng', 18)}}的其他基金
Collaborative Research: SaTC: CORE: Small: Differentially Private Data Synthesis: Practical Algorithms and Statistical Foundations
协作研究:SaTC:核心:小型:差分隐私数据合成:实用算法和统计基础
- 批准号:
2247795 - 财政年份:2023
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
I-Corps: Trustworthy Synthetic Data Generation
I-Corps:值得信赖的综合数据生成
- 批准号:
2317549 - 财政年份:2023
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
Collaborative Research: Nonparametric Bayesian Aggregation for Massive Data
协作研究:海量数据的非参数贝叶斯聚合
- 批准号:
1712907 - 财政年份:2017
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
Collaborative Research: Semiparametric ODE Models for Complex Gene Regulatory Networks
合作研究:复杂基因调控网络的半参数 ODE 模型
- 批准号:
1418202 - 财政年份:2014
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
CAREER: Bootstrap M-estimation in Semi-Nonparametric Models
职业:半非参数模型中的 Bootstrap M 估计
- 批准号:
1151692 - 财政年份:2012
- 资助金额:
$ 1.5万 - 项目类别:
Continuing Grant
General Semiparametric Inference via Bootstrap Sampling
通过 Bootstrap 采样进行一般半参数推理
- 批准号:
0906497 - 财政年份:2009
- 资助金额:
$ 1.5万 - 项目类别:
Standard Grant
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