Tong Wang

Orcid: 0000-0001-8687-4208

Affiliations:
  • University of Iowa, IA, USA
  • Massachusetts Institute of Technology, Cambridge, USA (PhD 2016)


According to our database1, Tong Wang authored at least 15 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Sparse and Faithful Explanations Without Sparse Models.
CoRR, 2024

2022
Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model.
Inf. Syst. Res., 2022

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects.
INFORMS J. Comput., 2022

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations.
Decis. Support Syst., 2022

2019
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs.
CoRR, 2019

2018
An Interpretable Model with Globally Consistent Explanations for Credit Risk.
CoRR, 2018

2017
A Bayesian Framework for Learning Rule Sets for Interpretable Classification.
J. Mach. Learn. Res., 2017

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect.
CoRR, 2017

2016
Finding patterns in features and observations: new machine learning models with applications in computational criminology, marketing, and medicine.
PhD thesis, 2016

Bayesian Rule Sets for Interpretable Classification.
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.
CoRR, 2015

Learning Optimized Or's of And's.
CoRR, 2015

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores.
Big Data, 2015

2013
Learning to Detect Patterns of Crime.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Detecting Patterns of Crime with Series Finder.
Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013


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