Seminars

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…

The modern retailing system is witnessing fast updating in product features and customer behaviors, entailing adaptive policies that can effectively…

Response time is readily available in computer-based discrete choice experiments, and the emergence of webcam-based eye-tracking will soon enable the…

We study the problem of distribution-free dependence detection and modeling through the new framework of binary expansion statistics (BEStat). The…

The strong law for independent and identically distributed random variables with finite variance is the intersection of Kolmogorov (1930) and…