53 citations · 115 across the 17 of their papers we have counts for
7 papers · 1 filter
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 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…
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar +1
In modern multilabel classification problems, each data instance belongs to a small number of classes from a large set of classes. In other words, these problems involve learning v…
Generalized Linear Rule Models
Dennis Wei, Sanjeeb Dash, Tian Gao +1
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate mode…
Low-Rank Boolean Matrix Approximation by Integer Programming
Reka Kovacs, Oktay Gunluk, Raphael Hauser
Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis. We consider the case of categorical variables,…
Optimal Generalized Decision Trees via Integer Programming
Oktay Gunluk, Jayant Kalagnanam, Minhan Li +2
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not alw…