This paper gives a review of concentration inequalities which are widely employed in non-
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …

This paper discusses two important limitations of the common practice of testing for
preexisting differences in trends (“pre-trends”) when using difference-in-differences and …

In recent years, log-concave density estimation via maximum likelihood estimation has
emerged as a fascinating alternative to traditional nonparametric smoothing techniques …

Sampling from various kinds of distribution is an issue of paramount importance in statistics
since it is often the key ingredient for constructing estimators, test procedures or confidence …

We prove an almost constant lower bound of the isoperimetric coefficient in the KLS
conjecture. The lower bound has the dimension dependency d^-o_d (1) d-od (1). When the …

We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain new convergence
guarantees under weaker assumptions than strong log-concavity: namely, our results hold …

We explore the learning process and behavior of an individual with unrealistically high
expectations (overconfidence) when outcomes also depend on an external fundamental that …

Given samples from an unknown distribution, p, is it possible to distinguish whether p
belongs to some class of distributions C versus p being far from every distribution in C? This …

We provide the first finite-particle convergence rate for Stein variational gradient descent
(SVGD), a popular algorithm for approximating a probability distribution with a collection of …

Optimal transport (OT) is a versatile framework for comparing probability measures, with
many applications to statistics, machine learning and applied mathematics. However, OT …