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Found 4 papers, 1 papers with code
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Our experiments show that by using SFGE we can: (1) deal with predictions that occur both in the objective function and in the constraints; and (2) effectively tackle two-stage stochastic optimization problems.
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena.
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses.
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy.
