We propose a robust and reliable evaluation metric for generative models called
Topological Precision and Recall (TopP&R, pronounced “topper”), which systematically …

Conformal prediction is a generic methodology for finite-sample valid distribution-free
prediction. This technique has garnered a lot of attention in the literature partly because it …

We study the multivariate nonparametric change point detection problem, where the data
are a sequence of independent-dimensional random vectors whose distributions are …

We analyze the convergence rates of nearest neighbor density estimation method, under
norm with. Our analysis includes two different cases depending on whether the support set …

Conformal prediction has been a very popular method of distribution-free predictive
inference in recent years in machine learning and statistics. Its popularity stems from the fact …

Topological data analysis (TDA) is an area of data science that focuses on using invariants
from algebraic topology to provide multiscale shape descriptors for geometric data sets such …

U-statistics represent a fundamental class of statistics from modelling quantities of interest
defined by multi-subject responses. U-statistics generalize the empirical mean of a random …

The focus of the present paper is on the uniform in bandwidth consistency of kernel-type
estimators of the regression function E (Ψ (Y)∣ X= x) derived by modern empirical process …

In this paper, we first introduce the multilayer random dot product graph (MRDPG) model,
which can be seen as an extension of the random dot product graph model to multilayer …

This paper addresses the problem of localizing and inferring multiple change points, in non-
parametric multivariate time series settings. Specifically, we consider a multivariate time …