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Efficient Learning of Nested Deep Hedging using Multiple Options
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.
Pairwise Supervision Can Provably Elicit a Decision Boundary
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.
Data Interpolating Prediction: Alternative Interpretation of Mixup
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks.
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
In this paper, we derive an unbiased risk estimator which can handle all of similarities/dissimilarities and unlabeled data.
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