TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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Presents a scalable randomized algorithm for geometric crossing minimization, including a theoretical approximation guarantee for vertex repositioning and experimental results on graphs with up to 13,000 edges.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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Geometric Crossing-Minimization -- A Scalable Randomized Approach
Presents a scalable randomized algorithm for geometric crossing minimization, including a theoretical approximation guarantee for vertex repositioning and experimental results on graphs with up to 13,000 edges.