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Found 4 papers, 2 papers with code
Learning to Learn in Interactive Constraint Acquisition
However, a large number of queries is still required to learn the model, which is a major limitation.
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system.
Guided Bottom-Up Interactive Constraint Acquisition
Second, we propose a probability-based method to guide query generation and show that it can significantly reduce the number of queries required to converge.
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.
