World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
3755
World Ranking
13714
National Ranking
1665

Yang Yu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Yang Yu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 150 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Yang Yu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Yang Yu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Yang Yu is affiliated with Nanjing University in China and has contributed extensively to the field of computer science, with a specific focus on artificial intelligence. Their work spans several subfields including artificial intelligence, computational theory and mathematics, management science and operations research, control and systems engineering, and information systems.

Yang Yu's research topics cover several advanced areas such as:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Data Stream Mining Techniques
  • Advanced Multi-Objective Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research
  • Advanced Bandit Algorithms Research
  • Adversarial Robustness in Machine Learning

Among recent papers authored or co-authored by Yang Yu are:

  • "COVID-19 Asymptomatic Infection Estimation" (2020), bioRxiv (Cold Spring Harbor Laboratory)
  • "QPLEX: Duplex Dueling Multi-Agent Q-Learning" (2020), arXiv (Cornell University)
  • "A survey on model-based reinforcement learning" (2024), Science China Information Sciences
  • "An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints" (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Error Bounds of Imitating Policies and Environments for Reinforcement Learning" (2021), IEEE Transactions on Pattern Analysis and Machine Intelligence

Yang Yu has published frequently in prominent venues, including:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Science China Information Sciences
  • Machine Learning

Frequent collaborators of Yang Yu include:

  • Yi-Qi Hu
  • Hong Qian
  • Zongzhang Zhang
  • Chao Qian
  • Fan-Ming Luo

Yang Yu has also contributed to academic literature through book publication, notably:

  • Derivative-Free Optimization (2025), published by Springer Nature

Best Publications

  • Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

    Shangce Gao;Yang Yu;Yirui Wang;Jiahai Wang

  • Taking Human out of Learning Applications: A Survey on Automated Machine Learning

    Quanming Yao;Mengshuo Wang;Hugo Jair Escalante;Isabelle Guyon

  • A two-layer surrogate-assisted particle swarm optimization algorithm

    Chaoli Sun;Yaochu Jin;Jianchao Zeng;Yang Yu

  • Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application

    Yujing Hu;Qing Da;Anxiang Zeng;Yang Yu

  • Diversity regularized ensemble pruning

    Nan Li;Yang Yu;Zhi-Hua Zhou

  • A multi-layered gravitational search algorithm for function optimization and real-world problems

    Yirui Wang;Shangce Gao;Mengchu Zhou;Yang Yu

  • Performance Evaluation of Model-Based Gait on Multi-View Very Large Population Database With Pose Sequences

    Weizhi An;Shiqi Yu;Yasushi Makihara;Xinhui Wu

  • QPLEX: Duplex Dueling Multi-Agent Q-Learning

    Jianhao Wang;Zhizhou Ren;Terry Liu;Yang Yu

  • Ensembling local learners ThroughMultimodal perturbation

    Zhi-Hua Zhou;Yang Yu

  • Subset selection by Pareto optimization

    Chao Qian;Yang Yu;Zhi-Hua Zhou

  • Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

    Shi-Yong Chen;Yang Yu;Qing Da;Jun Tan

  • QPLEX: Duplex Dueling Multi-Agent Q-Learning

    Jianhao Wang;Zhizhou Ren;Terry Liu;Yang Yu

  • Towards Sample Efficient Reinforcement Learning.

    Yang Yu

  • An analysis on recombination in multi-objective evolutionary optimization

    Chao Qian;Yang Yu;Zhi-Hua Zhou

  • Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning

    Jing-Cheng Shi;Yang Yu;Qing Da;Shi-Yong Chen

  • Pareto ensemble pruning

    Chao Qian;Yang Yu;Zhi-Hua Zhou

  • Learning with augmented class by exploiting unlabeled data

    Qing Da;Yang Yu;Zhi-Hua Zhou

  • A new approach to estimating the expected first hitting time of evolutionary algorithms

    Yang Yu;Zhi-Hua Zhou

  • Multi-label hypothesis reuse

    Sheng-Jun Huang;Yang Yu;Zhi-Hua Zhou

  • Evolutionary Learning: Advances in Theories and Algorithms

    Zhi-Hua Zhou;Yang Yu;Chao Qian

  • Spectrum of variable-random trees

    Fei Tony Liu;Kai Ming Ting;Yang Yu;Zhi-Hua Zhou

  • Derivative-free optimization via classification

    Yang Yu;Hong Qian;Yi-Qi Hu

  • A survey on model-based reinforcement learning

    Unknown

  • On the approximation ability of evolutionary optimization with application to minimum set cover

    Yang Yu;Xin Yao;Zhi-Hua Zhou

  • Bridging Machine Learning and Logical Reasoning by Abductive Learning

    Wang-Zhou Dai;Qiuling Xu;Yang Yu;Zhi-Hua Zhou

  • On Subset Selection with General Cost Constraints

    Chao Qian;Jing-Cheng Shi;Yang Yu;Ke Tang

  • On Reinforcement Learning for Full-Length Game of StarCraft

    Zhen-Jia Pang;Ruo-Ze Liu;Zhou-Yu Meng;Yi Zhang

  • Analyzing evolutionary optimization in noisy environments

    Chao Qian;Yang Yu;Zhi-Hua Zhou

  • Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions

    Hong Qian;Yang Yu

  • Error Bounds of Imitating Policies and Environments

    Tian Xu;Ziniu Li;Yang Yu

Frequent Co-Authors

Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Ke Tang
Ke Tang Southern University of Science and Technology
Xin Yao
Xin Yao Lingnan University
Tong Lu
Tong Lu Nanjing University
Hugo Jair Escalante
Hugo Jair Escalante National Institute of Astrophysics, Optics and Electronics
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Yaochu Jin
Yaochu Jin Westlake University
Kai Ming Ting
Kai Ming Ting Nanjing University
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Han Yu
Han Yu Nanyang Technological University

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