no code implementations • 11 Sep 2018 • Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama
A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.
no code implementations • 30 Jan 2019 • Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki, Masashi Sugiyama
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation.
no code implementations • 20 Sep 2023 • Kei Nakagawa, Masaya Abe, Seiichi Kuroki
However, the instability associated with the input parameter changes and estimation errors can deteriorate portfolio performance.