Add to ORKGThe concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law ...
This paper provides an overview of developments in robust optimization since 2007. It seeks to give ...
Many decision problems can be formulated as mathematical optimization models. While deterministic op...
Motivated by data-driven decision making and sampling problems, we investigate probabilistic interpr...
Traditional stochastic optimization assumes that the probability distribution of uncertainty is know...
We propose a formulation of a distributionally robust approach to model certain structural informat...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
Distributionally robust optimal control is a relatively new field of robust control that tries to ad...
We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data...
In this thesis, we study distributionally robust stochastic optimization (DRSO), a recent emerging f...
In this paper we study ambiguous chance constrained problems where the distributions of the random p...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approac...
We consider data-driven approaches that integrate a machine learning prediction model within distrib...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law ...
This paper provides an overview of developments in robust optimization since 2007. It seeks to give ...
Many decision problems can be formulated as mathematical optimization models. While deterministic op...
Motivated by data-driven decision making and sampling problems, we investigate probabilistic interpr...
Traditional stochastic optimization assumes that the probability distribution of uncertainty is know...
We propose a formulation of a distributionally robust approach to model certain structural informat...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
Distributionally robust optimal control is a relatively new field of robust control that tries to ad...
We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data...
In this thesis, we study distributionally robust stochastic optimization (DRSO), a recent emerging f...
In this paper we study ambiguous chance constrained problems where the distributions of the random p...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approac...
We consider data-driven approaches that integrate a machine learning prediction model within distrib...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law ...
This paper provides an overview of developments in robust optimization since 2007. It seeks to give ...