1 code implementation • 25 Jun 2021 • Reka A. Kovacs, Oktay Gunluk, Raphael A. Hauser
Binary matrix factorisation is an essential tool for identifying discrete patterns in binary data.
no code implementations • 13 Mar 2018 • Reka Kovacs, Oktay Gunluk, Raphael Hauser
Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis.
no code implementations • 10 Dec 2016 • Oktay Gunluk, Jayant Kalagnanam, Minhan Li, Matt Menickelly, Katya Scheinberg
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features.
no code implementations • 29 Oct 2017 • Vernon Austel, Sanjeeb Dash, Oktay Gunluk, Lior Horesh, Leo Liberti, Giacomo Nannicini, Baruch Schieber
In this study we introduce a new technique for symbolic regression that guarantees global optimality.
1 code implementation • NeurIPS 2020 • Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk
We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.
no code implementations • 9 Nov 2020 • Reka A. Kovacs, Oktay Gunluk, Raphael A. Hauser
Identifying discrete patterns in binary data is an important dimensionality reduction tool in machine learning and data mining.
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.
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 • 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 • 6 Feb 2023 • Connor Lawless, Oktay Gunluk
Clustering is an unsupervised learning task that aims to partition data into a set of clusters.