Tong Wang

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Cynthia RudinProfessor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke UniversityVerified email at cs.duke.edu
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
Perry MacNeilleResearch Engineer, Ford Motor CompanyVerified email at ford.com
Ronilo RagodosUniversity of IowaVerified email at uiowa.edu
Yaron ShaposhnikUniversity of RochesterVerified email at simon.rochester.edu
Kangcheng LinPh.D. Student, University of Illinois at Urbana-ChampaignVerified email at illinois.edu
Qihang LinThe University of IowaVerified email at uiowa.edu
Muriel MedardProfessor of EECS, MITVerified email at mit.edu
Sankeerth RampaAssociate Professor Health Care Administration, Rhode Island CollegeVerified email at ric.edu
Veerajalandhar AllareddyDuke UniversityVerified email at duke.edu
Veerasathpurush AllareddyUniversity of Illinois at Chicago College of DentistryVerified email at uic.edu
Chaofan ChenAssistant Professor of Computer Science, University of MaineVerified email at maine.edu
Dat HongNVIDIA CorporationVerified email at nvidia.com
Aditya BadhekaUniversity of IowaVerified email at uiowa.edu
Yu Jeffrey HuProfessor, Accenture Chair, Purdue UniversityVerified email at purdue.edu
Fujie JinIndiana UniversityVerified email at indiana.edu
Barrett W. ThomasProfessor, Department of Business Analytics, University of IowaVerified email at uiowa.edu
Marlin W. UlmerProfessor of Management Science, Otto-von-Guericke-Universität MagdeburgVerified email at ovgu.de
Xinwei ChenThe University of TulsaVerified email at utulsa.edu
Maytal Saar-TsechanskyThe University of Texas at AustinVerified email at mccombs.utexas.edu

Tong Wang

Yale University

Verified email at yale.edu - Homepage

interpretable machine learning reinforcement learning quantitative marketing


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A Bayesian Framework for Learning Rule Sets for Interpretable Classification T Wang, C Rudin, F Doshi, Y Liu, E Klampfl, P MacNeille Journal of Machine Learning Research 18 (70), 1-37, 2017	397*	2017
Learning to detect patterns of crime T Wang, C Rudin, D Wagner, R Sevieri Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013	182	2013
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations C Chen, K Lin, C Rudin, Y Shaposhnik, S Wang, T Wang Decision Support Systems, 2021	171*	2021
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute T Wang International Conference on Machine Learning, 6505-6514, 2019	83*	2019
Same-day delivery with fair customer service X Chen, T Wang, BW Thomas, MW Ulmer European journal of operational research 308 (2), 738-751, 2023	56*	2023
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model T Wang, Q Lin Journal of Machine Learning Research 22, 2021	56	2021
On the design of network coding for multiple two-way relaying channels Z Ding, T Wang, M Peng, W Wang, KK Leung IEEE Transactions on Wireless Communications 10 (6), 1820-1832, 2011	53	2011
Outcomes associated with peripherally inserted central catheters in hospitalised children: a retrospective 7-year single-centre experience A Badheka, J Bloxham, A Schmitz, B Freyenberger, T Wang, S Rampa, ... BMJ open 9 (8), e026031, 2019	47	2019
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects T Wang, C Rudin INFORMS Journal on Computing, 2022	41*	2022
Finding patterns with a rotten core: Data mining for crime series with cores T Wang, C Rudin, D Wagner, R Sevieri Big Data 3 (1), 3-21, 2015	40	2015
Detecting patterns of crime with series finder T Wang, C Rudin, D Wagner, R Sevieri Twenty-Seventh AAAI Conference on Artificial Intelligence Late Breaking, 2013	40	2013
Paralanguage classifier (PARA): An algorithm for automatic coding of paralinguistic nonverbal parts of speech in text AW Luangrath, Y Xu, T Wang Journal of Marketing Research 60 (2), 388-408, 2023	37	2023
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations T Wang (NeurIPS) Conference on Neural Information Processing Systems, 10858--10868, 2018	34*	2018
Network coded storage with multi-resolution codes M Medard, UJ Ferner, T Wang US Patent 9,607,003, 2017	29	2017
Learning optimized Or's of And's T Wang, C Rudin arXiv preprint arXiv:1511.02210, 2015	29	2015
ProtoryNet-interpretable text classification via prototype trajectories D Hong, T Wang, S Baek Journal of Machine Learning Research 24 (264), 1-39, 2023	27*	2023
Evaluating the Effectiveness of Marketing Campaigns for Malls Using A Novel Interpretable Machine Learning Model T Wang, C He, F Jin, YJ Hu Information Systems Research, 2021	24	2021
Dental anomaly detection using intraoral photos via deep learning R Ragodos, T Wang, C Padilla, JT Hecht, FA Poletta, IM Orioli, CJ Buxó, ... Scientific Reports 12 (1), 1-1, 2022	23	2022
Interpretable Companions for Black-Box Models D Pan, T Wang, S Hara AISTATS, 2020	20	2020
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness T Wang, M Saar-Tsechansky arXiv preprint arXiv:2011.08398, 2020	14	2020

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