Multi-task Optimization

Multi-Task Optimization is a computational learning framework that jointly optimizes multiple related tasks by leveraging their shared representations and inter-task dependencies to improve generalization performance compared to learning each task separately.

516

Publications

36.1K

Citations

2K

Authors

535

Institutions

Publications per year

2017–2026

427

Authors

2K

  1. MG
    Maoguo Gong

    Xidian University, China

    13 publications · 892 citations

    13

    h-index

  2. KC
    Kay Chen Tan

    National University of Singapore, Singapore

    12 publications · 1.4K citations

    12

    h-index

  3. ZZ
    Zexuan Zhu

    Shenzhen University, China

    12 publications · 658 citations

    12

    h-index

  4. YO
    Yew-Soon Ong

    Nanyang Technological University, Singapore

    11 publications · 1.9K citations

    11

    h-index

  5. JZ
    Jinghui Zhong

    South China University of Technology, China

    10 publications · 1.1K citations

    10

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 66 publications · 3.4K citations

    30

    h-index

  2. IEEE Transactions on Evolutionary Computation

    33 publications · 2.9K citations

    29

    h-index

  3. IEEE Transactions on Cybernetics

    17 publications · 1.5K citations

    15

    h-index

  4. IEEE Access

    13 publications · 543 citations

    13

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 1.1K citations

    12

    h-index

1–5