Online Stochastic Optimization

Online Stochastic Optimization is a stochastic optimization framework in which decisions are made sequentially in real-time under uncertainty, with the distinguishing feature that the underlying probability distributions of random variables are either unknown or revealed incrementally through partial observations, requiring adaptive decision-making at each stage.

244

Publications

105.5K

Citations

606

Authors

259

Institutions

Publications per year

2017–2026

105

Authors

606

  1. PV
    Pascal Van Hentenryck

    Brown University, United States

    6 publications · 278 citations

    6

    h-index

  2. EH
    Elad Hazan

    Princeton University, United States

    5 publications · 9K citations

    5

    h-index

  3. RB
    Russell Bent

    Los Alamos National Laboratory, United States

    5 publications · 258 citations

    5

    h-index

  4. NL
    Na Li

    Harvard University, United States

    4 publications · 131 citations

    4

    h-index

  5. YL
    Yingying Li

    Harvard University, United States

    4 publications · 131 citations

    4

    h-index

1–5 of 606

Venues

  1. arXiv (Cornell University)

    Repository · 43 publications · 1.7K citations

    21

    h-index

  2. IEEE Transactions on Automatic Control

    12 publications · 472 citations

    12

    h-index

  3. Journal of Machine Learning Research

    JMLR · 6 publications · 872 citations

    6

    h-index

  4. Neural Information Processing Systems

    Conference · 6 publications · 237 citations

    6

    h-index

  5. Operations Research

    5 publications · 360 citations

    5

    h-index

1–5