Journal of the Association for Computing Machinery Issues Research Articles in November 2020 Edition
November 24, 2020
November 24, 2020
NEW YORK, Nov. 24 -- The Journal of the Association for Computing Machinery, a journal that says it provides principles of computer science, published research articles, including the following topics, in its November 2020 edition:
Machine Learning:
* Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes
Approximation Algorithms:
* Approximating Edit Distance Within Constant Factor in Truly Sub . . .
Machine Learning:
* Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes
Approximation Algorithms:
* Approximating Edit Distance Within Constant Factor in Truly Sub . . .