User-friendly introduction to PAC-Bayes bounds
P Alquier - Foundations and Trends® in Machine Learning, 2024 - nowpublishers.com
Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …
some weights, that is, to some probability distribution. Randomized predictors are obtained …
A reduced-rank approach to predicting multiple binary responses through machine learning
TT Mai - Statistics and Computing, 2023 - Springer
This paper investigates the problem of simultaneously predicting multiple binary responses
by utilizing a shared set of covariates. Our approach incorporates machine learning …
by utilizing a shared set of covariates. Our approach incorporates machine learning …
Recent Advances in Statistical Theory and Applications
A Wong, X Shi - Entropy, 2023 - mdpi.com
Complex data pose unique challenges for data processing. Moreover, traditional inferential
methods need to be modified or extended in order to fully utilize information provided by the …
methods need to be modified or extended in order to fully utilize information provided by the …
Concentration properties of fractional posterior in 1-bit matrix completion
TT Mai - arXiv preprint arXiv:2404.08969, 2024 - arxiv.org
The problem of estimating a matrix based on a set of its observed entries is commonly
referred to as the matrix completion problem. In this work, we specifically address the …
referred to as the matrix completion problem. In this work, we specifically address the …
On properties of fractional posterior in generalized reduced-rank regression
TT Mai - arXiv preprint arXiv:2404.17850, 2024 - arxiv.org
Reduced rank regression (RRR) is a widely employed model for investigating the linear
association between multiple response variables and a set of predictors. While RRR has …
association between multiple response variables and a set of predictors. While RRR has …