2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.4.5 | ||
| Paper Title | Learning Additive Noise Channels: Generalization Bounds and Algorithms | ||
| Authors | Nir Weinberger, Massachusetts Institute of Technology, 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 | An additive noise channel is considered, in which the noise distribution is unknown and does not known to belong to any parametric family. The problem of designing a codebook and a generalized minimal distance decoder (which is parameterized by a covariance matrix) based on samples of the noise is considered. High probability generalization bounds for the error probability loss function, as well as for a hinge-type surrogate loss function are provided. A stochastic-gradient based alternating-minimization algorithm for the latter loss function is presented. Bounds on the average empirical error and generalization error are provided for a Gibbs based algorithm that gradually expurgates codewords from a large initial codebook to obtain a smaller codebook with improved error probability. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
