8 citations · 16 across the 4 of their papers we have counts for
4 papers
Efficient Learning of Nested Deep Hedging using Multiple Options
Masanori Hirano, Kentaro Imajo, Kentaro Minami +1
Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple o…
Pairwise Supervision Can Provably Elicit a Decision Boundary
Han Bao, Takuya Shimada, Liyuan Xu +2
Similarity learning is a general problem to elicit useful representations by predicting the relationship between a pair of patterns. This problem is related to various important pr…
Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi +1
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between a…
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Takuya Shimada, Han Bao, Issei Sato +1
Pairwise similarities and dissimilarities between data points might be easier to obtain than fully labeled data in real-world classification problems, e.g., in privacy-aware situat…