Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

Humans have a powerful and mysterious capacity to reason.

This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.