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Found 27 papers, 11 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.
Holy Grail 2.0: From Natural Language to Constraint Models
Twenty-seven years ago, E. Freuder highlighted that "Constraint programming represents one of the closest approaches computer science has yet made to the Holy Grail of programming: the user states the problem, the computer solves it".
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.
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples)
We build on a recently proposed method for stepwise explaining solutions of Constraint Satisfaction Problems (CSP) in a human-understandable way.
A Divide-Align-Conquer Strategy for Program Synthesis
We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
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.
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
We build on a recently proposed method for explaining solutions of constraint satisfaction problems.
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.
A framework for step-wise explaining how to solve constraint satisfaction problems
We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP.
Knowledge Refactoring for Inductive Program Synthesis
We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it.
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.
Learning to rank for uplift modeling
We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework.
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems.
Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining
In this paper, in addition to specifying a dominance relation, we introduce the ability to specify an incomparability condition.
Vehicle routing by learning from historical solutions
Even in the case of changes in the client sets, our method is able to find solutions that are closer to the actual route plans than when using distances, and hence, solutions that would require fewer manual changes to transform into the actual route plan.
Learning Relational Representations with Auto-encoding Logic Programs
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
Solution Dominance over Constraint Satisfaction Problems
We define Constraint Dominance Problems (CDPs) as CSPs with a dominance relation, that is, a preorder over the solutions of the CSP.
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
Detailed experiments show how this approach outperforms existing CP as well as specialized systems for SPM, and that the gain in efficiency translates directly into increased efficiency for other settings such as mining with regular expressions.
The Inductive Constraint Programming Loop
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.
Constraint-based sequence mining using constraint programming
We investigate the use of constraint programming as general framework for this task.
