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cs.LG2026
Generating Benchmark Health Data Using a Tabular Diffusion Transformer
Hao Yan, Lisa Pilgram, Dan Liu +3
Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic ta…
cs.LG2024
FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning
Jordan Slessor, Dezheng Kong, Xiaofen Tang +2
Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving w…