1 citations · 1 across the 5 of their papers we have counts for
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Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data
Zilong Zhao, Abdul Raheem, Jiayu Li +5
Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, t…
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Jiayu Li, Bingyin Zhao, Zilong Zhao +3
Transformers have shown impressive results in tabular data generation. However, they lack domain-specific inductive biases which are critical for preserving the intrinsic character…
Laplace Transform Interpretation of Differential Privacy
Rishav Chourasia, Uzair Javaid, Biplap Sikdar
We introduce a set of useful expressions of Differential Privacy (DP) notions in terms of the Laplace transform of the privacy loss distribution. Its bare form expression appears i…
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Jiayu Li, Zilong Zhao, Kevin Yee +2
Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion a…
CombU: A Combined Unit Activation for Fitting Mathematical Expressions with Neural Networks
Jiayu Li, Zilong Zhao, Kevin Yee +2
The activation functions are fundamental to neural networks as they introduce non-linearity into data relationships, thereby enabling deep networks to approximate complex data rela…