EAGER: Multi-objective generation of synthetic time series data to boost model robustness and data privacy

EAGER：合成时间序列数据的多目标生成，以提高模型的稳健性和数据隐私

• 批准号: 2240615
• 负责人: Diane Cook
• 金额: $ 20万
• 依托单位: Washington State University
• 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: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240615&HistoricalAwards=false
• 关键词: EAGER; Multi; objective; generation; synthetic

Machine learning models require a sufficient amount and diversity of training data to ensure robustness and minimize bias. A dearth of data can negatively impact the predictive performance of machine learning algorithms. Because researchers recognize the surrogate role offered by synthetic data generators, they have been creating methods to generate increasingly realistic data proxies. Synthetic data can be automatically generated that balance the need to maximize multiple objectives. The goal of this project is to design a synthetic data generator that creates realistic individual and time series data to optimize cooperating or competing objectives in an adversarial manner. Building on this contribution, the algorithm will also be evaluated as a mechanism to increase model robustness, improve privacy preservation, and decrease model bias.The outcomes of this project include the design of a novel multi-agent generative adversarial network (GAN) architecture, called HydraGAN, that balances multiple, possibly competing, data goals. While researchers have investigated constrained versions of multi-criteria GANs, this project will introduce a novel method that facilitates balancing any number of data criteria using a multi-agent GAN. A second outcome of the project will be a formal proof that the system will reach a Nash equilibrium during training. Furthermore, the HydraGAN algorithm will be enhanced to not only explore a traditional i.i.d. data generation format but also handle the more complex nature of time-series data, representing one of the first efforts to create multi-agent GANs for time series data. Multiple discriminator agents will be defined for the multi-agent system. Exploring beyond the traditional criteria of sample realism, cooperating or competing agents will address the underexplored constraints of privacy preservation, distribution realism, and meeting of diversity constraints. The project will demonstrate the utility of the created synthetic data for tackling machine learning challenges including data sparsity and representation bias. While traditional performance metrics focus on the realism of individual data samples, this work will introduce an Area Under the Radar Chart metric, or AURC, to evaluate the data based on an arbitrary number of data quality criteria.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.

机器学习模型需要足够数量和多样性的训练数据，以确保鲁棒性并最大限度地减少偏差。数据的缺乏会对机器学习算法的预测性能产生负面影响。由于研究人员认识到合成数据生成器提供的代理角色，他们一直在创建方法来生成越来越真实的数据代理。可以自动生成综合数据，以平衡最大化多个目标的需求。该项目的目标是设计一个合成数据生成器，创建现实的个人和时间序列数据，以对抗的方式优化合作或竞争目标。在此基础上，该算法还将作为一种机制进行评估，以提高模型的鲁棒性，改善隐私保护，并减少模型的偏见。该项目的成果包括一个新的多代理生成对抗网络（GAN）架构的设计，称为HydraGAN，平衡多个，可能竞争，数据目标。虽然研究人员已经研究了多标准GAN的约束版本，但该项目将引入一种新的方法，该方法可以使用多代理GAN来平衡任何数量的数据标准。该项目的第二个成果将是正式证明系统将在训练期间达到纳什均衡。此外，HydraGAN算法将得到增强，不仅可以探索传统的i.i.d.数据生成格式，但也处理时间序列数据的更复杂性质，代表了为时间序列数据创建多代理GAN的首批努力之一。将为多代理系统定义多个代理。探索超越传统标准的样本现实主义，合作或竞争的代理将解决隐私保护，分布现实主义和会议的多样性约束的不足。该项目将展示所创建的合成数据在解决机器学习挑战方面的实用性，包括数据稀疏性和表示偏差。虽然传统的性能指标侧重于单个数据样本的真实性，但这项工作将引入雷达图下的区域指标，或AURC，以根据任意数量的数据质量标准评估数据。该奖项反映了NSF的法定使命，并被认为值得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来支持。