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Found 10 papers, 6 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.
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP
These preferences are in the form of arc probabilities, i. e., the more preferred a route is, the higher is the joint probability.
Learn-n-Route: Learning implicit preferences for vehicle routing
Even in the case of changes in the customer sets, our method is able to find solutions that are closer to the actual routings than when using only distances, and hence, solutions that require fewer manual changes when transformed into practical routings.
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.
Interior Point Solving for LP-based prediction+optimisation
Solving optimization problems is the key to decision making in many real-life analytics applications.
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.
Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support
We have developed "risk-o-meter", a framework, based on machine learning and natural language processing to review and assess risks of any legal document.
