A distributionally robust perspective on uncertainty quantification and chance constrained programming

2015/03/20 by Grani A. Hanasusanto, Vladimir Roitch, Daniel Kuhn +2 · 11 citations
Decision Sciences · Mathematics · #Risk and Portfolio Optimization #Probabilistic and Robust Engineering Design #Fuzzy Systems and Optimization

paper · doi:10.1007/s10107-015-0896-z

Citations

• Data-driven robust optimization
• On Distributionally Robust Chance-Constrained Linear Programs
• Robust Estimation of a Location Parameter

Cited by

• On distributionally robust chance constrained programs with Wasserstein distance
• Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity
• On the Impossibility of Statistically Improving Empirical Optimization: A Second-Order Stochastic Dominance Perspective
• Tutorials on Advanced Optimization Methods
• Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
• On Distributionally Robust Chance Constrained Programs with Wasserstein Distance
• New Algorithms And Fast Implementations To Approximate Stochastic Processes
• Wasserstein Distributionally Robust Optimization: Theory and\n Applications in Machine Learning
• Bayesian distributionally robust variational inequalities: regularization and quantification
• Tractable Reformulations of Distributionally Robust Two-stage Stochastic Programs with ∞-Wasserstein Distance
• Distributionally Robust Shape and Topology Optimization