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

机器学习模型需要足够数量和多样性的训练数据，以确保鲁棒性和最小化偏差。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，他们一直在创造方法来生成越来越现实的数据代理。可以自动生成合成数据，以平衡最大化多个目标的需要。该项目的目标是设计一个合成数据生成器，该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，该算法还将被评估为一种提高模型鲁棒性、改善隐私保护和减少模型偏差的机制。该项目的成果包括设计一种新的多智能体生成对抗网络（GAN）架构，称为HydraGAN，它可以平衡多个可能相互竞争的数据目标。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，该方法可以使用多智能体GAN来平衡任何数量的数据标准。该项目的第二个结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，还可以处理时间序列数据的更复杂性质，这是为时间序列数据创建多智能体gan的首次努力之一。对于多智能体系统，将定义多个判别器智能体。探索超越样本现实主义的传统标准，合作或竞争代理将解决隐私保护，分配现实主义和满足多样性约束的未充分探索的约束。该项目将展示创建的合成数据在解决机器学习挑战（包括数据稀疏性和表示偏差）方面的效用。传统的性能指标侧重于单个数据样本的真实性，而这项工作将引入雷达图下面积指标（AURC），基于任意数量的数据质量标准来评估数据。该奖项反映了美国国家科学基金会的法定使命，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。

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.