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cs.LG2026
The Composition Gap in Dataset Distillation
Guang Li, Takahiro Ogawa, Miki Haseyama
Dataset distillation compresses a training set into a small synthetic set, usually evaluated one at a time. In federated and data-governance settings, several parties distill their…
cs.LG2022★ 2 cited
Gromov-Wasserstein Autoencoders
Nao Nakagawa, Ren Togo, Takahiro Ogawa +1
Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tas…