My research focuses on developing interpretable machine learning algorithms and pipelines to help people make better decisions in high-stakes situations.
I work on optimizing interpretable models such as decision trees and generalized additive models with ℓ0-based sparsity constraints. These algorithms are able to find simple, interpretable models that achieve accuracy comparable to black-box models.
My recent research focuses on the Rashomon set, which enumerates, stores, and visualizes all models within ε of the optimal loss. This paradigm enables seamless user interaction. Models that satisfy arbitrary user constraints (e.g. monotonicity, fairness) can be found by simply filtering over the Rashomon set instead of retraining. Model-free analysis can be conducted on metrics such as variable importance.
Areas: Machine Learning, Optimization, Human-Model Interaction
Topics: Interpretability, Rashomon Sets, Logical Models
Applications: Healthcare, Finance, Criminal Justice
(* indicates co-first authors, equal contribution)
The Amazing Things that Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
ICML 2024 (Spotlight)
Spotlight Paper at ICML 2024
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Chudi Zhong*, Zhi Chen*, Jiachang Liu, Margo Seltzer, Cynthia Rudin
NeurIPS 2023
Paper Code VideoOKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems
Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin
NeurIPS 2023 (Spotlight)
Spotlight Paper at NeurIPS 2023
PaperExploring the Whole Rashomon Set of Sparse Decision Trees
Rui Xin*, Chudi Zhong*, Zhi Chen*, Takuya Takagi, Margo Seltzer, Cynthia Rudin
NeurIPS 2022 (Oral)
Oral Paper at NeurIPS 2022
Finalist, Data Mining Best Student Paper Award, INFORMS, 2022
Paper CodeFasterRisk: Fast and Accurate Interpretable Risk Scores
Jiachang Liu*, Chudi Zhong*, Boxuan Li, Margo Seltzer, Cynthia Rudin
NeurIPS 2022
Paper CodeTimberTrek: Exploring and Curating Trustworthy Decision Trees with Interactive Visualization
Zijie Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer
IEEE VIS 2022
Paper Code DemoFast Sparse Classification for Generalized Linear and Additive Models
Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin
AISTATS 2022
Paper CodeFast Sparse Decision Tree Optimization via Reference Ensembles
Hayden McTavish*, Chudi Zhong*, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo Seltzer
AAAI 2022
Paper CodeInterpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong
Statistics Surveys 2022
Paper