Lectures on Stochastic Programming: Modeling and Theory, Second Edition

By Alexander Shapiro, Darinka Dentcheva †, Andrzej Ruszczynski. 2014

442 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Mathematical Optimization

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In Management Science and Operations Research

1. Ryan-Rhys Griffiths
2. José Miguel Hernández-Lobato
3. Yiming Ying
4. Tianbao Yang
5. Zoubin Ghahramani
6. Peter I. Frazier

In Mathematical Optimization

1. José Miguel Hernández-Lobato
2. Yiming Ying
3. Tianbao Yang

Citations in the corpus

• Phase Transitions in Transfer Learning for High-Dimensional Perceptrons (2021, unknown): “Combining this with [29, Theorem 7.46], the above asymptotic function is continuous in x > 0 .”
• Constrained Bayesian Optimization for Automatic Chemical Design (2017, background): “nction values. The same principles also apply to the unknown black-box objective function. Problems that adhere to the two points above come under the general class of stochastic programming problems [105]. A natural goal for a statistical model of this kind of problem is to attempt to minimize the ”
• AUC Maximization in the Era of Big Data and AI: A Survey (2022, background): “The existing works consider two learning settings: online setting similar to stochastic approximation in conventional literature [148], and oline setting similar to stochastic average approximation in conventional literature [125].”