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arXiv Machine Learning By Xiaofeng Lin, Seungbae Kim, Zhuoya Li, Zachary DeSoto, Charles Fleming, Guang Cheng

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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