Logistic reduced rank regression is a useful data analysis tool when we have multiple binary
response variables and a set of predictors. In this paper, we describe logistic reduced rank …

For supervised classification we propose to use restricted multidimensional unfolding in a
multinomial logistic framework. Where previous research proposed similar models based on …

Privacy-preserving machine learning is one class of cryptographic methods that aim to
analyze private and sensitive data while keeping privacy, such as homomorphic logistic …

Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones.
Incorporating specific corruptions into the data augmentation pipeline can improve …

Quantile regression is a robust and practically useful way to efficiently model quantile
varying correlation and predict varied response quantiles of interest. This article constructs …

In this manuscript, we extend our previous work on privacy-preserving regression to address
multi-output regression problems using data encrypted under a fully homomorphic …

Privacy-preserving regression in machine learning is a crucial area of research, aimed at
enabling the use of powerful machine learning techniques while protecting individuals' …

Many statistical inference and machine learning methods rely on the ability to optimize an
expectation functional, whose explicit form is intractable. The typical method for conducting …