2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.4.3 | ||
| Paper Title | On Learning Parametric Non-Smooth Continuous Distributions | ||
| Authors | Sudeep Kamath, PDT Partners, United States; Alon Orlitsky, University of California San Diego, United States; Venkatadheeraj Pichapati, Apple Inc., United States; Ehsan Zobeidi, University of California San Diego, 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 | With the eventual goal of better understanding learning rates of general continuous distributions, we derive the first essentially min-max optimal estimators and learning rates for several natural classes of parametric non-smooth continuous distributions under KL divergence. In particular, we show that unlike the folk theorem of 1/2n learning-rate increase per distribution parameter, non-smooth distribution exhibit a wide range of learning rates. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
