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Found 6 papers, 1 papers with code
Fair Minimum Representation Clustering
Clustering is an unsupervised learning task that aims to partition data into a set of clusters.
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficients can be predicted based on context information.
Cluster Explanation via Polyhedral Descriptions
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features.
Interpretable Clustering via Multi-Polytope Machines
To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance.
Interpretable and Fair Boolean Rule Sets via Column Generation
This paper considers the learning of Boolean rules in disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) as an interpretable model for classification.
Fair Decision Rules for Binary Classification
In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints.
