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Dynamic optimization with side information
Through a novel measure concentration result for a class of machine learning methods, we prove that the proposed approach is asymptotically optimal for multi-period stochastic programming with side information.
From Predictions to Prescriptions in Multistage Optimization Problems
In this paper, we introduce a framework for solving finite-horizon multistage optimization problems under uncertainty in the presence of auxiliary data.
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data.
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