Suivre
Chenhao Tan
Titre
Citée par
Citée par
Année
Explaining machine learning classifiers through diverse counterfactual explanations
RK Mothilal, A Sharma, C Tan
Proceedings of the 2020 conference on fairness, accountability, and …, 2020
13052020
User-level sentiment analysis incorporating social networks
C Tan, L Lee, J Tang, L Jiang, M Zhou, P Li
Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011
5952011
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
V Lai, C Tan
Proceedings of the conference on fairness, accountability, and transparency …, 2019
4602019
Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions
C Tan, V Niculae, C Danescu-Niculescu-Mizil, L Lee
Proceedings of the 25th international conference on world wide web, 613-624, 2016
4482016
Creative writing with a machine in the loop: Case studies on slogans and stories
E Clark, AS Ross, C Tan, Y Ji, NA Smith
Proceedings of the 23rd International Conference on Intelligent User …, 2018
3172018
Towards a science of human-ai decision making: a survey of empirical studies
V Lai, C Chen, QV Liao, A Smith-Renner, C Tan
arXiv preprint arXiv:2112.11471, 2021
293*2021
Preserving causal constraints in counterfactual explanations for machine learning classifiers
D Mahajan, C Tan, A Sharma
arXiv preprint arXiv:1912.03277, 2019
2672019
Causal reasoning and large language models: Opening a new frontier for causality
E Kıcıman, R Ness, A Sharma, C Tan
arXiv preprint arXiv:2305.00050, 2023
2502023
The effect of wording on message propagation: Topic-and author-controlled natural experiments on Twitter
C Tan, L Lee, B Pang
Proceedings of ACL, 2014
2482014
Selecting directors using machine learning
I Erel, LH Stern, C Tan, MS Weisbach
The Review of Financial Studies 34 (7), 3226-3264, 2021
2362021
Neural models for documents with metadata
D Card, C Tan, NA Smith
arXiv preprint arXiv:1705.09296, 2017
175*2017
Active example selection for in-context learning
Y Zhang, S Feng, C Tan
arXiv preprint arXiv:2211.04486, 2022
1652022
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
V Lai, H Liu, C Tan
arXiv preprint arXiv:2001.05871, 2020
1632020
On the interplay between social and topical structure
DM Romero, C Tan, J Ugander
Proc. 7th International AAAI Conference on Weblogs and Social Media (ICWSM), 2013
1472013
Human-ai collaboration via conditional delegation: A case study of content moderation
V Lai, S Carton, R Bhatnagar, QV Liao, Y Zhang, C Tan
Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022
1462022
Understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making
H Liu, V Lai, C Tan
Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2), 1-45, 2021
1462021
Social action tracking via noise tolerant time-varying factor graphs
C Tan, J Tang, J Sun, Q Lin, F Wang
Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010
1432010
Dynamic entity representations in neural language models
Y Ji, C Tan, S Martschat, Y Choi, NA Smith
arXiv preprint arXiv:1708.00781, 2017
1282017
Towards unifying feature attribution and counterfactual explanations: Different means to the same end
R Kommiya Mothilal, D Mahajan, C Tan, A Sharma
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 652-663, 2021
1192021
All who wander: On the prevalence and characteristics of multi-community engagement
C Tan, L Lee
Proceedings of the 24th International Conference on World Wide Web, 1056-1066, 2015
1182015
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