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Chao Qian

24 papers hereh-index 284.5k citations103 works total

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

author position
  • sole author2
  • first author7
  • middle author11
  • last author4

Across the 24 of 24 papers where every author was matched, so the position is known.

fields
  • cs.NE14
  • cs.LG3
  • cs.MA2
  • cs.AI1
  • cs.CC1
  • cs.CL1
same name
  • Chao Qian — 16 papers, h 6
  • Chao Qian — 16 papers, h 8
  • Chao Qian — 8 papers, h 6
  • Chao Qian — 6 papers, h 5
  • Chao Qian — 6 papers, h 4
  • Chao Qian — 4 papers, h 1

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
20162023
most citedStochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

10 citations · 34 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.NE2019

Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions

Chao Qian

As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes…

stat.ML2019★ 1 cited

Bayesian Optimization using Pseudo-Points

Chao Qian, Hang Xiong, Ke Xue

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, robotics. BO usually model…

cs.NE2019

On the Robustness of Median Sampling in Noisy Evolutionary Optimization

Chao Bian, Chao Qian, Yang Yu +1

Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective ev…

cs.CC2019

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

Chao Bian, Chao Qian, Ke Tang +1

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand…

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