64 citations · 165 across the 20 of their papers we have counts for
20 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…
VT-GAN: Cooperative Tabular Data Synthesis using Vertical Federated Learning
Zilong Zhao, Han Wu, Aad Van Moorsel +1
This paper presents the application of Vertical Federated Learning (VFL) to generate synthetic tabular data using Generative Adversarial Networks (GANs). VFL is a collaborative app…
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…
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…
Fabricated Flips: Poisoning Federated Learning without Data
Jiyue Huang, Zilong Zhao, Lydia Y. Chen +1
Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decen…
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN
Zilong Zhao, Jiyue Huang, Stefanie Roos +1
Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images. Due to data not being centrally available, Multi-Discriminator (MD)-…
ComicGAN: Text-to-Comic Generative Adversarial Network
Ben Proven-Bessel, Zilong Zhao, Lydia Chen
Drawing and annotating comic illustrations is a complex and difficult process. No existing machine learning algorithms have been developed to create comic illustrations based on de…
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…
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
Zilong Zhao, Robert Birke, Aditya Kunar +1
Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training da…
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
CTAB-GAN: Effective Table Data Synthesizing
Zilong Zhao, Aditya Kunar, Hiek Van der Scheer +2
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its f…
An Exploratory Analysis on Users' Contributions in Federated Learning
Jiyue Huang, Rania Talbi, Zilong Zhao +3
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core pr…
Event-Based Control for Online Training of Neural Networks
Zilong Zhao, Sophie Cerf, Bogdan Robu +1
Convolutional Neural Network (CNN) has become the most used method for image classification tasks. During its training the learning rate and the gradient are two key factors to tun…
QActor: On-line Active Learning for Noisy Labeled Stream Data
Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi +2
Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regu…
Feedback Control for Online Training of Neural Networks
Zilong Zhao, Sophie Cerf, Bogdan Robu +1
Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation i…
RAD: On-line Anomaly Detection for Highly Unreliable Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…