Detecting Patterns of Crime with Series Finder. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (AAAI (Late-Breaking Developments), 2013)

Learning to Detect Patterns of Crime. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (ECML/PKDD (3), 2013)

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (Big Data, 2015)

Learning Optimized Or's of And's. - Tong Wang 011; Cynthia Rudin (CoRR, 2015)

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (CoRR, 2015)

Bayesian Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (ICDM, 2016)

Finding patterns in features and observations: new machine learning models with applications in computational criminology, marketing, and medicine. - Tong Wang 011 (2016)

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. - Tong Wang 011; Cynthia Rudin (CoRR, 2017)

A Bayesian Framework for Learning Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (J. Mach. Learn. Res., 2017)

An Interpretable Model with Globally Consistent Explanations for Credit Risk. - Chaofan Chen; Kangcheng Lin; Cynthia Rudin; Yaron Shaposhnik; Sijia Wang; Tong Wang 011 (CoRR, 2018)

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. - Tong Wang 011; Qihang Lin (CoRR, 2019)

Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models. - Hassan Rafique; Tong Wang 011; Qihang Lin (CoRR, 2019)

Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs. - Tong Wang 011; Fujie Jin; Yu (Jeffrey) Hu; Yuan Cheng (CoRR, 2019)

Transparency Promotion with Model-Agnostic Linear Competitors. - Hassan Rafique; Tong Wang 011; Qihang Lin; Arshia Singhani (ICML, 2020)

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. - Tong Wang 011; Qihang Lin (J. Mach. Learn. Res., 2021)

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. - Ronilo J. Ragodos; Tong Wang 011; Qihang Lin; Xun Zhou 001 (NeurIPS, 2022)

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. - Chaofan Chen; Kangcheng Lin; Cynthia Rudin; Yaron Shaposhnik; Sijia Wang; Tong Wang 011 (Decis. Support Syst., 2022)

Disjunctive Rule Lists. - Ronilo J. Ragodos; Tong Wang 011 (INFORMS J. Comput., 2022)

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. - Tong Wang 011; Cynthia Rudin (INFORMS J. Comput., 2022)

Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model. - Tong Wang 011; Cheng He 005; Fujie Jin; Yu (Jeffrey) Hu (Inf. Syst. Res., 2022)

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

Improving Decision Sparsity. - Yiyang Sun; Tong Wang 011; Cynthia Rudin (NeurIPS, 2024)