1 citations · 1 across the 3 of their papers we have counts for
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Comparing interpretability and explainability for feature selection
Jack Dunn, Luca Mingardi, Ying Daisy Zhuo
A common approach for feature selection is to examine the variable importance scores for a machine learning model, as a way to understand which features are the most relevant for m…
Detecting Racial Bias in Jury Selection
Jack Dunn, Ying Daisy Zhuo
To support the 2019 U.S. Supreme Court case "Flowers v. Mississippi", APM Reports collated historical court records to assess whether the State exhibited a racial bias in striking…
Interpretable Predictive Maintenance for Hard Drives
Maxime Amram, Jack Dunn, Jeremy J. Toledano +1
Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans. This limits…
Optimal Survival Trees
Dimitris Bertsimas, Jack Dunn, Emma Gibson +1
Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these…
Optimal Policy Trees
Maxime Amram, Jack Dunn, Ying Daisy Zhuo
We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literatur…