Adversarial Feature Matching for Text Generation. - Yizhe Zhang 002; Zhe Gan; Kai Fan 002; Zhi Chen 009; Ricardo Henao; Dinghan Shen; Lawrence Carin (ICML, 2017)

Concept whitening for interpretable image recognition. - Zhi Chen 009; Yijie Bei; Cynthia Rudin (Nat. Mach. Intell., 2020)

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. - Zhi Chen 009; Sarah Tan; Harsha Nori; Kori Inkpen; Yin Lou; Rich Caruana (PKDD/ECML Workshops (1), 2021)

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. - Cynthia Rudin; Chaofan Chen; Zhi Chen 009; Haiyang Huang 003; Lesia Semenova; Chudi Zhong (CoRR, 2021)

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. - Zhi Chen 009; Alexander C. Ogren; Chiara Daraio; L. Catherine Brinson; Cynthia Rudin (CoRR, 2021)

2D elastodynamic metamaterials. - Alexander C. Ogren; Zhi Chen 009; L. Catherine Brinson; Mary Bastawrous; Cynthia Rudin; Chiara Daraio (UCI Machine Learning Repository, 2021)

Exploring the Whole Rashomon Set of Sparse Decision Trees. - Rui Xin 002; Chudi Zhong; Zhi Chen 009; Takuya Takagi; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 2022)

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. - Zijie J. Wang; Chudi Zhong; Rui Xin 002; Takuya Takagi; Zhi Chen 009; Duen Horng Chau; Cynthia Rudin; Margo I. Seltzer (IEEE VIS (Short Papers), 2022)

SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. - Haiyang Huang 003; Zhi Chen 009; Cynthia Rudin (CoRR, 2022)

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? - Zhi Chen 009; Sarah Tan; Urszula Chajewska; Cynthia Rudin; Rich Caruana (CHIL, 2023)

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models. - Chudi Zhong; Zhi Chen 009; Jiachang Liu 001; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 2023)

Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. - Zhi Chen 009; Chudi Zhong; Margo I. Seltzer; Cynthia Rudin (CoRR, 2023)

Human-in-the-loop Machine Learning System via Model Interpretability. - Zhi Chen 009 (2023)

Sparse and Faithful Explanations Without Sparse Models. - Yiyang Sun; Zhi Chen 009; Vittorio Orlandi; Tong Wang 011; Cynthia Rudin (AISTATS, 2024)

Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning. - Mary Bastawrous; Zhi Chen 009; Alexander C. Ogren; Chiara Daraio; Cynthia Rudin; L. Catherine Brinson (CoRR, 2024)