Seminars

Estimating the full (conditional) distribution is crucial to many applications. However, existing methods such as quantile regression typically struggle with…

Modern data science pipelines are often severely constrained in resources, both statistical (e.g., poor-quality input data due to outliers) as…

Genome-wide genetic models for outcomes of interest such as a disease state have been widely used, and have led to…

Modern data science spans various parts of computer science, mathematics, and applications. This talk will bring a sharp focus on…

With advances in scientific computing, computer simulations are increasingly used for investigating complex physical phenomena. For many such applications, scientific…

Although the statistical literature on causality is more focused on forward causal questions about the effects of causes, reverse causal…

Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of…

Conformal prediction is a popular technique for constructing prediction intervals with distribution-free coverage guarantees. The coverage is marginal, holding on…

Sampling is one of the most fundamental problems in computational inference. Beyond Bayesian statistics, it has numerous applications in modern…

Datasets are central to the development of statistical learning theory, and the evolution of models. The burgeoning success of modern…