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Found 5 papers, 1 papers with code
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into data preprocessing to reduce privacy risks of the synthetic dataset generation via DDMs.
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
However, these models face challenges in generating large attributed graphs due to the complex attribute-structure correlations and the large size of these graphs.
Differentially Private Synthetic Data Using KD-Trees
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
