Synthesizing tables for supervised learning

Yaoyu Zhu, Guoliang Li, Jianhua Feng, Nan Tang

Abstract: Synthesizing tables–which generates fake tables that resemble real tables–is important for supervised machine learning (ML) with many practical applications, such as generating more data as a way of data augmentation or publishing synthetic tables while preserving the privacy of real tables. A fundamental question is: Given a real table, can we synthesize a table such that ML models trained on the real or the synthetic table perform similarly on an unseen test table?

External IDs:dblp:journals/fcsc/ZhuLFT26