Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

Arnab Bhattacharyya; Sutanu Gayen; Kuldeep S Meel; N. V. Vinodchandran

NeurIPS 2020poster28 citations

Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S Meel, N. V. Vinodchandran

Abstract

We design efficient distance approximation algorithms for several classes of well-studied structured high-dimensional distributions. Specifically, we present algorithms for the following problems (where dTV is the total variation distance):

BibTeX

@inproceedings{NEURIPS2020_a8acc287,
 author = {Bhattacharyya, Arnab and Gayen, Sutanu and Meel, Kuldeep S and Vinodchandran, N. V. },
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {14699--14711},
 publisher = {Curran Associates, Inc.},
 title = {Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/a8acc28734d4fe90ea24353d901ae678-Paper.pdf},
 volume = {33},
 year = {2020}
}