Reliable Machine Learning via Structured Distributionally Robust Optimization Data sets used to train machine learning (ML) models often suffer from sampling biases and underrepresent marginalized groups. Standard are trained optimize average performance perform poorly on tail subpopulations. In “Distributionally Losses for Latent Covariate Mixtures,” John Duchi, Tatsunori Hashimoto, Hongseok N...

OBJECTIVE The aim of the study is to examine the distribution of integrated covariate and its association with blood pressure (BP) among children in Anhui province, China, and assess the predictive value of integrated covariate to children hypertension. METHODS A total of 2,828 subjects (1,588 male and 1,240 female) aged 7-17 years participated in this study. Height, weight, waistline, hiplin...

The appraisals of treatment-covariate interaction have theoretical and substantial implications in all scientific fields. Methodologically, the detection of interaction between categorical treatment levels and continuous covariate variables is analogous to the homogeneity of regression slopes test in the context of ANCOVA. A fundamental assumption of ANCOVA is that the regression slopes associa...

The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, smal...

In supervised learning there is a typical presumption that the training and test points are taken from the same distribution. In practice this assumption is commonly violated. The situations where the training and test data are from different distributions is called covariate shift. Recent work has examined techniques for dealing with covariate shift in terms of minimisation of generalisation e...

Covariate adjustment using linear models for continuous outcomes in randomized trials has been shown to increase efficiency and power over the unadjusted method in estimating the marginal effect of treatment. However, for binary outcomes, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on the logistic regression models ...

Estimating the marginal and joint densities of long-term average intakes different dietary components is an important problem in nutritional epidemiology. Since these variables cannot be directly measured, data are usually collected form 24-hour recalls intakes. The estimating density latent from their observed but error contaminated then becomes a multivariate deconvolution densities. underlyi...

A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, or active learning scenarios. The violation of this assumption— known as the covariate shift—causes a heavy bias in standard generalization error estimation schemes such as cross-validati...