no code implementations • 20 May 2023 • Masanori Hirano, Kentaro Imajo, Kentaro Minami, Takuya Shimada
That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions.
no code implementations • 11 Jun 2020 • Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.
no code implementations • ICLR Workshop LLD 2019 • Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, Sosuke Kobayashi
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks.
no code implementations • 26 Apr 2019 • Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama
In this paper, we derive an unbiased risk estimator which can handle all of similarities/dissimilarities and unlabeled data.