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stat.ML2026
ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
Xiaofeng Lin, Seungbae Kim, Zhuoya Li +3
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 c…
stat.ML2023★ 5 cited
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh +2
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesi…