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researcher

Ming Xiao

49 papers hereh-index 4711.4k citations317 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author31
  • last author16

Across the 48 of 49 papers where every author was matched, so the position is known.

fields
  • cs.IT29
  • cs.NI6
  • eess.SP5
  • cs.LG4
  • cs.DC2
  • cs.PF1
same name
  • Ming Xiao — 30 papers, h 16
  • Ming Xiao — 27 papers, h 6
  • Ming Xiao — 23 papers, h 10
  • Ming Xiao — 19 papers
  • Ming Xiao — 10 papers, h 4
  • Ming Xiao — 9 papers, h 13

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122022
most citedOn Maximizing Sensor Network Lifetime by Energy Balancing

43 citations · 225 across the 49 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.DC2019

Decentralized Consensus Optimization Based on Parallel Random Walk

Yu Ye, Hao Chen, Zheng Ma +1

The alternating direction method of multipliers (ADMM) has recently been recognized as a promising approach for large-scale machine learning models. However, very few results study…

cs.NI2019★ 1 cited

Traffic-aware Two-stage Queueing Communication Networks: Queue Analysis and Energy Saving

Nan Qi, Nikolaos I. Miridakis, Ming Xiao +3

To boost energy saving for the general delay-tolerant IoT networks, a two-stage and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware N-thre…

cs.LG2019★ 1 cited

Mobility-aware Content Preference Learning in Decentralized Caching Networks

Yu Ye, Ming Xiao, Mikael Skoglund

Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. To determine the caching scheme for decentralized cachi…

cs.LG2019★ 10 cited

Decentralized Multi-Task Learning Based on Extreme Learning Machines

Yu Ye, Ming Xiao, Mikael Skoglund

In multi-task learning (MTL), related tasks learn jointly to improve generalization performance. To exploit the high learning speed of extreme learning machines (ELMs), we apply th…

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