Estimating Certain Integral Probability Metric (IPM) is as Hard as Estimating under the IPM

11/02/2019
by   Tengyuan Liang, et al.
0

We study the minimax optimal rates for estimating a range of Integral Probability Metrics (IPMs) between two unknown probability measures, based on n independent samples from them. Curiously, we show that estimating the IPM itself between probability measures, is not significantly easier than estimating the probability measures under the IPM. We prove that the minimax optimal rates for these two problems are multiplicatively equivalent, up to a loglog (n)/log (n) factor.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/27/2019

On the Minimax Optimality of Estimating the Wasserstein Metric

We study the minimax optimal rate for estimating the Wasserstein-1 metri...
research
02/12/2019

Minimax rates in outlier-robust estimation of discrete models

We consider the problem of estimating the probability distribution of a ...
research
03/30/2020

Bounding the expectation of the supremum of empirical processes indexed by Hölder classes

We obtain upper bounds on the expectation of the supremum of empirical p...
research
06/25/2018

Towards Optimal Estimation of Bivariate Isotonic Matrices with Unknown Permutations

Many applications, including rank aggregation, crowd-labeling, and graph...
research
05/13/2023

On Semi-Supervised Estimation of Distributions

We study the problem of estimating the joint probability mass function (...
research
05/07/2019

Minimax Hausdorff estimation of density level sets

Given a random sample of points from some unknown density, we propose a ...
research
05/27/2022

Learning with Stochastic Orders

Learning high-dimensional distributions is often done with explicit like...

Please sign up or login with your details

Forgot password? Click here to reset