2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.4.1 | ||
| Paper Title | Analysis of K Nearest Neighbor KL Divergence Estimation for Continuous Distributions | ||
| Authors | Puning Zhao, Lifeng Lai, University of California Davis, United States | ||
| Session | L.4: Distribution Learning | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Estimating Kullback-Leibler divergence from identically and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
