Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models

Abstract

Synthetic data refers to data generated by a mechanism designed to mimic the distribution of the raw data. In the era of generative artificial intelligence, the significance of synthetic data has dramatically increased. It offers numerous advantages in data science and machine learning tasks. For instance, synthetic data can be used to augment original datasets, helping to alleviate data scarcity and potentially enhancing the performance of predictive models. Synthetic data can also be tailored to meet standard privacy criteria, enabling data sharing and collaboration across different parties and platforms. For a systematic evaluation of synthetic data applied to downstream tasks, this thesis studies the "generation effect" --- how errors from generative models affect the accuracy/power of the downstream analysis. We provide practical and valid methods of utilizing synthetic data for both prediction and inference tasks, supported by both theoretical insights as well as numerical experiments.