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Found 6 papers, 4 papers with code
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.
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.
Probability estimation and structured output prediction for learning preferences in last mile delivery
Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further.
Decision-Focused Learning: Through the Lens of Learning to Rank
In the last years decision-focused learning framework, also known as predict-and-optimize, have received increasing attention.
Contrastive Losses and Solution Caching for Predict-and-Optimize
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.
Hybrid Classification and Reasoning for Image-based Constraint Solving
We explore the trade-off between the power of the classifier and the power of the constraint reasoning, as well as further integration through the additional use of structural knowledge.
