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- W4387618032 abstract "Abstract This paper investigates the problem of simultaneously predicting multiple binary responses by utilizing a shared set of covariates. Our approach incorporates machine learning techniques for binary classification, without making assumptions about the underlying observations. Instead, our focus lies on a group of predictors, aiming to identify the one that minimizes prediction error. Unlike previous studies that primarily address estimation error, we directly analyze the prediction error of our method using PAC-Bayesian bounds techniques. In this paper, we introduce a pseudo-Bayesian approach capable of handling incomplete response data. Our strategy is efficiently implemented using the Langevin Monte Carlo method. Through simulation studies and a practical application using real data, we demonstrate the effectiveness of our proposed method, producing comparable or sometimes superior results compared to the current state-of-the-art method." @default.
- W4387618032 created "2023-10-14" @default.
- W4387618032 creator A5028022746 @default.
- W4387618032 date "2023-10-13" @default.
- W4387618032 modified "2023-10-15" @default.
- W4387618032 title "A reduced-rank approach to predicting multiple binary responses through machine learning" @default.
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- W4387618032 doi "https://doi.org/10.1007/s11222-023-10314-3" @default.
- W4387618032 hasPublicationYear "2023" @default.
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