Distributed Ranking with Communications: Approximation Analysis and Applications
AbstractLearning 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.
How to Cite
Chen, H., Wang, Y., Wang, Y., & Zheng, F. (2021). Distributed Ranking with Communications: Approximation Analysis and Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7037-7045. https://doi.org/10.1609/aaai.v35i8.16866
AAAI Technical Track on Machine Learning I