1 citations · 1 across the 10 of their papers we have counts for
10 papers
ArborEnum: Decision Tree Rashomon Sets over Continuous Features
Zakk Heile, Hayden McTavish, Margo Seltzer +1
The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, fe…
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Yixiao Wang, Hayden McTavish, Varun Babbar +2
Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing w…
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
Zakk Heile, Hayden McTavish, Margo Seltzer +1
Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains. For the remaining settings, how…
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
Zakk Heile, Hayden McTavish, Varun Babbar +2
Standard machine learning pipelines often admit many near-optimal models. These "Rashomon sets" pose a range of challenges and opportunities for uncertainty-aware, robust decision…
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…
Leveraging Predictive Equivalence in Decision Trees
Hayden McTavish, Zachery Boner, Jon Donnelly +2
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predi…
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
Jon Donnelly, Zhicheng Guo, Alina Jade Barnett +3
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part mode…
Near Optimal Decision Trees in a SPLIT Second
Varun Babbar, Hayden McTavish, Cynthia Rudin +1
Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is…
Interpretable Generalized Additive Models for Datasets with Missing Values
Hayden McTavish, Jon Donnelly, Margo Seltzer +1
Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing dat…
Fast Sparse Decision Tree Optimization via Reference Ensembles
Hayden McTavish, Chudi Zhong, Reto Achermann +4
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse d…