This work studies for which low-fidelity outputs, one should obtain high-fidelity outputs, if the goal is to estimate the probability density function of the latter, especially when it comes to the distribution tails and extremes.
We propose a generalized Bayes framework that avoids full probability modeling of all survival outcomes by using an RMST-targeted loss function that depends on a collection of inverse probability of censoring weights (IPCW).
This study proposes a novel method for forecasting a scalar variable based on high-dimensional predictors that is applicable to various data distributions.
Valid statistical inference is crucial for decision-making but difficult to obtain in supervised learning with multimodal data, e. g., combinations of clinical features, genomic data, and medical images.
Motivated by ecology applications in which latent features correspond to which species are discovered in a sample, we propose a new class of dependent infinite latent feature models.
We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts.
We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in assessments of algorithm performance and quantify the statistical bias that results from error in these imputed group probabilities.
Bayesian variable selection defines a posterior distribution on the possible subsets of the variables (which are usually termed models) to express uncertainty about which variables are strongly linked to the response.
Colocalization analyses assess whether two traits are affected by the same or distinct causal genetic variants in a single gene region.
The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications.