Tong Wang

According to our database, Tong Wang authored at least 22 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improving Decision Sparsity.

Yiyang Sun, Tong Wang, Cynthia Rudin

CoRR, 2024

Sparse and Faithful Explanations Without Sparse Models.

Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2022

Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model.

Tong Wang, Cheng He, Fujie Jin, Yu (Jeffrey) Hu

Inf. Syst. Res., 2022

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects.

Tong Wang, Cynthia Rudin

INFORMS J. Comput., 2022

Disjunctive Rule Lists.

Ronilo J. Ragodos, Tong Wang

INFORMS J. Comput., 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

Decis. Support Syst., 2022

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping.

Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model.

Tong Wang, Qihang Lin

J. Mach. Learn. Res., 2021

2020

Transparency Promotion with Model-Agnostic Linear Competitors.

Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs.

Tong Wang, Fujie Jin, Yu (Jeffrey) Hu, Yuan Cheng

CoRR, 2019

Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models.

Hassan Rafique, Tong Wang, Qihang Lin

CoRR, 2019

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model.

Tong Wang, Qihang Lin

CoRR, 2019

2018

An Interpretable Model with Globally Consistent Explanations for Credit Risk.

Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

CoRR, 2018

2017

A Bayesian Framework for Learning Rule Sets for Interpretable Classification.

Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

J. Mach. Learn. Res., 2017

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect.

Tong Wang, Cynthia Rudin

CoRR, 2017

2016

Finding patterns in features and observations: new machine learning models with applications in computational criminology, marketing, and medicine.

Tong Wang

PhD thesis, 2016

Bayesian Rule Sets for Interpretable Classification.

Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

2015

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems.

Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

CoRR, 2015

Learning Optimized Or's of And's.

Tong Wang, Cynthia Rudin

CoRR, 2015

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores.

Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri

Big Data, 2015

2013

Learning to Detect Patterns of Crime.

Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Detecting Patterns of Crime with Series Finder.

Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013