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Found 3 papers, 2 papers with code
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters.
Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier.
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