6 papers
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…
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…
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…
CUTTANA: Scalable Graph Partitioning for Faster Distributed Graph Databases and Analytics
Milad Rezaei Hajidehi, Sraavan Sridhar, Margo Seltzer
Graph partitioning plays a pivotal role in various distributed graph processing applications, including graph analytics, graph neural network training, and distributed graph databa…
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova +7
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many rea…
Optimal Sparse Survival Trees
Rui Zhang, Rui Xin, Margo Seltzer +1
Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve huma…