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Xin Yao

11 papers hereh-index 201.7k citations40 works total

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

author position
  • middle author2
  • last author9

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.SE4
  • cs.AI1
  • cs.NE1
  • cs.SI1
same name
  • Xin Yao — 26 papers, h 47
  • Xin Yao — 10 papers
  • Xin Yao — 10 papers, h 6
  • Xin Yao — 9 papers, h 3
  • Xin Yao — 8 papers, h 5
  • Xin Yao — 6 papers, h 15

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
20162022
most citedHow to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

102 citations · 373 across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 3 cited

Adaptive Initialization Method for K-means Algorithm

Jie Yang, Yu-Kai Wang, Xin Yao +1

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine th…

cs.NE2019

A Simple Yet Effective Approach to Robust Optimization Over Time

Lukáš Adam, Xin Yao

Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time. Most of the existing algorithms use particle…

cs.SI2019★ 8 cited

Learning Topological Representation for Networks via Hierarchical Sampling

Guoji Fu, Chengbin Hou, Xin Yao

The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a…

cs.LG2019★ 4 cited

Representation Learning for Heterogeneous Information Networks via Embedding Events

Guoji Fu, Bo Yuan, Qiqi Duan +1

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, exis…

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