Kolmogorov–Smirnov Test: Overview

By V. Berger, Yanyan Zhou. 2014

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6ce71bcfe7569adb2725329a42cfb0b008731763

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Scott Niekum
2. Emma Brunskill
3. Bruno Castro da Silva
4. Philip S. Thomas
5. Yash Chandak
6. Eric Learned-Miller
7. Sergey Levine
8. Lihong Li

In Statistics and Probability

1. Eric Learned-Miller
2. Philip S. Thomas
3. Bruno Castro da Silva
4. Yash Chandak
5. Emma Brunskill
6. Scott Niekum

Citations in the corpus

• Universal Off-Policy Evaluation (2021, methodology): “To make the problem tractable, similar to prior work on characterizing distribution shifts [10], we assume that the Kolmogorov-Smirnov distance between F (1) π and F (2) π is bounded.”