10 citations · 18 across the 5 of their papers we have counts for
6 papers
Fair Clustering with Minimum Representation Constraints
Connor Lawless, Oktay Gunluk
Clustering is a well-studied unsupervised learning task that aims to partition data points into a number of clusters. In many applications, these clusters correspond to real-world…
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret
Connor Lawless, Angela Zhou
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficient…
Cluster Explanation via Polyhedral Descriptions
Connor Lawless, Oktay Gunluk
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited in…
Interpretable Clustering via Multi-Polytope Machines
Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen +2
Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use…
Interpretable and Fair Boolean Rule Sets via Column Generation
Connor Lawless, Sanjeeb Dash, Oktay Gunluk +1
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. An i…
Fair Decision Rules for Binary Classification
Connor Lawless, Oktay Gunluk
In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive na…