15 citations · 86 across the 22 of their papers we have counts for
14 papers · 1 filter
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…
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…
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federico Lucchetti, Jérémie Decouchant, Maria Fernandes +2
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulat…
CTAB-GAN+: Enhancing Tabular Data Synthesis
Zilong Zhao, Aditya Kunar, Robert Birke +1
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) limit its full effectiven…
DTGAN: Differential Private Training for Tabular GANs
Aditya Kunar, Robert Birke, Zilong Zhao +1
Tabular generative adversarial networks (TGAN) have recently emerged to cater to the need of synthesizing tabular data -- the most widely used data format. While synthetic tabular…
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning. In this paper, we show -- theoretically and through simulations -- that Sha…