6 citations · 8 across the 9 of their papers we have counts for
9 papers
Falling Trees: A Model Class for Interpretable Risk Prioritization
Varun Babbar, Zachery Boner, Margo Seltzer +1
Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule lists (FRLs), which are orde…
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…
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…
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…
SoK: The Faults in our Graph Benchmarks
Puneet Mehrotra, Vaastav Anand, Daniel Margo +2
Graph-structured data is prevalent in domains such as social networks, financial transactions, brain networks, and protein interactions. As a result, the research community has pro…
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…
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…
NetShaper: A Differentially Private Network Side-Channel Mitigation System
Amir Sabzi, Rut Vora, Swati Goswami +3
The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet applications. However, these protocols cannot prevent ne…