1.2k citations · 5k across the 152 of their papers we have counts for
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Trustworthy Feature Importance Avoids Unrestricted Permutations
Emanuele Borgonovo, Francesco Cappelli, Xuefei Lu +2
Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose…
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
Simon D. Nguyen, Hayden McTavish, Kentaro Hoffman +2
Active learning reduces labeling costs by selecting samples that maximize information gain. A dominant framework, Query-by-Committee (QBC), typically relies on perturbation-based d…
Leo Breiman, the Rashomon Effect, and the Occam Dilemma
Cynthia Rudin
In the famous Two Cultures paper, Leo Breiman provided a visionary perspective on the cultures of ''data models'' (modeling with consideration of data generation) versus ''algorith…
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Caroline Wang, Bin Han, Bhrij Patel +1
Objectives: We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike p…
Reducing Exploration of Dying Arms in Mortal Bandits
Stefano Tracà, Cynthia Rudin, Weiyu Yan
Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the s…
Towards Practical Lipschitz Bandits
Tianyu Wang, Weicheng Ye, Dawei Geng +1
Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains. In this paper, we present a framework for L…