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Found 3 papers, 1 papers with code
Compiling Stochastic Constraint Programs to And-Or Decision Diagrams
In this paper we show how identifying and exploiting these identical subproblems can simplify solving them and leads to a compact representation of the solution.
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making.
Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams
For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency.
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