Publications

† student under my supervision, * correponding author

Preprints

2025

  1. SSRN
    Why it Works: Can LLM Hypotheses Improve AI Generated Marketing Content?
    Tong Wang, K. Sudhir, and Hengguang Zhou
    SSRN, 2025

2024

  1. Arxiv
    The Case for Value-Driven AI Advisors and a Framework for Producing Them
    Nicholas Wolczynski, Maytal Saar-Tsechansky, and Tong Wang
    Arxiv, 2024
  2. Arxiv
    Can Advanced LLMs Coach Smaller LLMs? Knowledge Distillation for Goal-Oriented Dialogs
    Tong Wang, K Sudhir, and Dat Hong
    Arxiv, 2024

    Presented at NeurIPS workshop on Interpretable AI: Past, Present and Future, 2024

  3. SSRN

Marketing and Management

2024

  1. POM
    Making Early and Accurate Deep Learning Predictions to Help Disadvantaged Individuals in Medical Crowdfunding
    Tong Wang, Fujie Jin, Yu Jeffrey Hu, Lu Feng, and Yuan Cheng
    Production and Operations Management, 2024

2023

  1. JMR
    Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text
    Andrea Webb Luangrath, Yixiang Xu, and Tong Wang
    Journal of Marketing Research, 2023

2021

  1. ISR
    Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model
    Tong Wang, Cheng He, Fujie Jin, and Yu Jeffrey Hu
    Information Systems Research, 2021

Machine Learning Methods

2025

  1. NeurIPS
    ProtoPairNet: Interpretable Regression through Prototypical Pair Reasoning
    Rose Gurung, Ronilo Ragodos†, Chiyu Ma, Tong Wang, and Chaofan Chen
    Advances in Neural Information Processing Systems, 2025
  2. IJOC
    PIE—Partially Interpretable Estimators with Refinement
    Tong Wang, Jingyi Yang, Yunyi Li, and Boxiang Wang
    INFORMS Journal on Computing, 2025

2024

  1. NeurIPS
    Improving Decision Sparsity
    Yiyang Sun, Tong Wang, and Cynthia Rudin
    Advances in Neural Information Processing Systems, 2024
  2. AISTATS
    Sparse and Faithful Explanations Without Sparse Models
    Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, and Cynthia Rudin
    International conference on artificial intelligence and statistics, 2024

    Winner of Data Mining Best Paper Award, INFORMS, 2023.

2023

  1. JMLR
    Interpretable Sequence Classification via Prototype Trajectory
    Dat Hong†, Tong Wang*, and Stephen Baek
    Journal of Machine Learning Research, 2023
    Code

2022

  1. NeurIPS
    ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
    Ronilo Ragodos†, Tong Wang*, Qihang Lin, and Xun Zhou
    Advances in Neural Information Processing Systems, 2022
  2. IJOC
    Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects
    Tong Wang and Cynthia Rudin
    INFORMS Journal on Computing, 2022
  3. SIGKDD
    AdaAX: Explaining Recurrent Neural Networks by Learning Automata with Adaptive States
    Dat Hong†, Alberto Maria Segre, and Tong Wang*
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022

2021

  1. JMLR
    Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
    Tong Wang and Qihang Lin
    Journal of Machine Learning Research, 2021

2020

  1. AISTATS
    Interpretable Companions for Black-box Models
    Danqing Pan, Tong Wang, and Satoshi Hara
    International conference on artificial intelligence and statistics, 2020
  2. ICML
    Transparency Promotion with Model-agnostic Linear Competitors
    Hassan Rafique†, Tong Wang*, Qihang Lin, and Arshia Singhani
    International Conference on Machine Learning, 2020

    Runner-up for Best Paper Award at INFORMS Workshop on Data Science, 2019.

2019

  1. ICML
    Gaining Free or Low-cost Interpretability with Interpretable Partial Substitute
    Tong Wang
    International Conference on Machine Learning, 2019
    Code

2018

  1. NeurIPS
    Multi-value Rule Sets for Interpretable Classification with Feature-efficient Representations
    Tong Wang
    Advances in neural information processing systems, 2018
    Code

2017

  1. JMLR
    A Bayesian Framework for Learning Rule Sets for Interpretable Classification
    Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille
    Journal of Machine Learning Research, 2017
    Code

Machine Learning Applications

2025

  1. SIGSPATIAL
    ConPro-GAIL: Interpretable Policy Learning via Conceptual Prototyping for Human Spatiotemporal Decision Understanding
    Ronilo Ragodos†, Xun Zhou, Tong Wang, Yajun Pan, and Jun Luo
    ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2025

2023

  1. EJOR
    Same-day Delivery with Fair Customer Service
    Xinwei Chen, Tong Wang, Barrett W Thomas, and Marlin W Ulmer
    European Journal of Operational Research, 2023

2022

  1. DSS
    A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-explanations
    Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, and Tong Wang
    Decision Support Systems, 2022

    This project is the winner of FICO Recognition Award for the FICO xML Challenge, for building an interpretable model that beats black-box models. See the blog on FICO website about this entry.

  2. IJOC
    Disjunctive Rule Lists
    Ronilo Ragodos† and Tong Wang
    INFORMS Journal on Computing, 2022
  3. Sci. Rep.
    Dental Anomaly Detection Using Intraoral Photos Via Deep Learning
    Ronilo Ragodos†, Tong Wang*, George Wehby, Seth Weinberg, Deborah Dawson, Mary Mzrazita, and 2 more authors
    Scientific Reports, 2022

2019

  1. Socius
    Humans in the Loop: Incorporating Expert and Crowd-Sourced Knowledge for Predictions Using Survey Data
    Anna Filippova, Connor Gilroy, Ridhi Kashyap, Antje Kirchner, Allison Morgan, Kivan Polimis, and 2 more authors
    Socius, 2019

2018

  1. Am. J. Surg
    Prevalence and Predictors of C. Difficile Infections in Those Who Had Major Surgical Procedures in USA: Analysis Using the Traditional and Machine Learning Methods
    Veerajalandhar Allareddy, Tong Wang, Sankeerth Rampa, Jennifer Caplin, Romesh Nalliah, Aditya Badheka, and 1 more author
    American Journal of Surgery, 2018

2015

  1. BigData
    Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores
    Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri
    Big Data, 2015

2013

  1. ECML-PKDD
    Learning to Detect Patterns of Crime
    Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013

    This work is the second place winner of “Doing Good with Good OR”, INFORMS 2015, and is reported in several media including WIRED.com and Wiki Crime Analysis. Ideas from this work were adopted by the NYPD in their algorithm Patternizr (please see this paper for the details), which operates live in New York City since 2016.