no code implementations • 6 Feb 2023 • Connor Lawless, Oktay Gunluk
Clustering is an unsupervised learning task that aims to partition data into a set of clusters.
1 code implementation • 9 Nov 2022 • 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 coefficients can be predicted based on context information.
no code implementations • 17 Oct 2022 • Connor Lawless, Oktay Gunluk
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features.
no code implementations • 10 Dec 2021 • Connor Lawless, Jayant Kalagnanam, Lam M. Nguyen, Dzung Phan, Chandra Reddy
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.
no code implementations • 16 Nov 2021 • Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei
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.
no code implementations • 3 Jul 2021 • Connor Lawless, Oktay Gunluk
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.