Distributed Ranking with Communications: Approximation Analysis and Applications

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.

Authors

• Hong Chen
• Yingjie Wang College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
• Yulong Wang College of Computer Science, Nankai University Tianjin Media Computing Center
• Feng Zheng Southern University of Science and Technology

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