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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 — 24 papers, h 47
  • Xin Yao — 10 papers, h 6
  • Xin Yao — 8 papers
  • Xin Yao — 7 papers, h 5
  • Xin Yao — 6 papers, h 15
  • Xin Yao — 5 papers, h 3

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 citedMitigating Unfairness via Evolutionary Multi-objective Ensemble Learning

35 citations · 53 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 35 cited

Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning

Qingquan Zhang, Jialin Liu, Zeqi Zhang +3

In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the traini…

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.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…

cs.LG2018

Evolutionary Generative Adversarial Networks

Chaoyue Wang, Chang Xu, Xin Yao +1

Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from trai…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.