15 citations · 54 across the 13 of their papers we have counts for
17 papers
TabVFL: Improving Latent Representation in Vertical Federated Learning
Mohamed Rashad, Zilong Zhao, Jeremie Decouchant +1
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model d…
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Zilong Zhao, Robert Birke, Lydia Chen
Tabular data synthesis is crucial for addressing privacy and security concerns in industries reliant on tabular data. While recent advancements adopt large language models (LLMs) f…
Permutation-Invariant Tabular Data Synthesis
Yujin Zhu, Zilong Zhao, Robert Birke +1
Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tab…
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy
Han Wu, Zilong Zhao, Lydia Y. Chen +1
Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning methodology, since it avoids exchanging data between participants, but instead exc…
FCT-GAN: Enhancing Table Synthesis via Fourier Transform
Zilong Zhao, Robert Birke, Lydia Y. Chen
Synthetic tabular data emerges as an alternative for sharing knowledge while adhering to restrictive data access regulations, e.g., European General Data Protection Regulation (GDP…
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners
Tianyi Liu, Size Hou, Jiayuan Zhu +2
Thanks to the capacity for long-range dependencies and robustness to irregular shapes, vision transformers and deformable convolutions are emerging as powerful vision techniques of…