Distributed Ranking with Communications: Approximation Analysis and Applications
Abstract
Learning theory of distributed algorithms has recently attracted enormous attention in the machine learning community. However, most of existing works focus on learning problem with pointwise loss and does not consider the communication among local processors. In this paper, we propose a new distributed pairwise ranking with communication (called DLSRank-C) based on the Newton-Raphson iteration, and establish its learning rate analysis in probability. Theoretical and empirical assessments demonstrate the effectiveness of DLSRank-C under mild conditions.
Cite
Text
Chen et al. "Distributed Ranking with Communications: Approximation Analysis and Applications." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I8.16866Markdown
[Chen et al. "Distributed Ranking with Communications: Approximation Analysis and Applications." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/chen2021aaai-distributed/) doi:10.1609/AAAI.V35I8.16866BibTeX
@inproceedings{chen2021aaai-distributed,
title = {{Distributed Ranking with Communications: Approximation Analysis and Applications}},
author = {Chen, Hong and Wang, Yingjie and Wang, Yulong and Zheng, Feng},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {7037-7045},
doi = {10.1609/AAAI.V35I8.16866},
url = {https://mlanthology.org/aaai/2021/chen2021aaai-distributed/}
}