9 citations · 15 across the 25 of their papers we have counts for
10 papers · 1 filter
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…
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
Yi Li, Han Liu, Mingfeng Fan +3
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We…
PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing
Bhavya Kohli, Biplab Sikdar
Message Passing Neural Networks (MPNNs) have achieved strong performance on tasks involving relational data. However, small perturbations to graph structure can significantly alter…
Towards Irreversible Machine Unlearning for Diffusion Models
Xun Yuan, Zilong Zhao, Jiayu Li +3
Diffusion models are renowned for their state-of-the-art performance in generating synthetic images. However, concerns related to safety, privacy, and copyright highlight the need…
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…
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…
VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data
Xun Yuan, Zilong Zhao, Prosanta Gope +1
In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot b…
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
Xun Yuan, Yang Yang, Prosanta Gope +2
In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunc…
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
Asif Iqbal, Prosanta Gope, Biplab Sikdar
Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-bas…