| Explaining machine learning classifiers through diverse counterfactual explanations RK Mothilal, A Sharma, C Tan Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 1201 | 2020 |
| 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 | 589 | 2011 |
| 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 | 426 | 2016 |
| 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 | 425 | 2019 |
| 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 | 302 | 2018 |
| 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 | 251* | 2021 |
| Preserving causal constraints in counterfactual explanations for machine learning classifiers D Mahajan, C Tan, A Sharma arXiv preprint arXiv:1912.03277, 2019 | 246 | 2019 |
| 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 | 246 | 2014 |
| Selecting directors using machine learning I Erel, LH Stern, C Tan, MS Weisbach The Review of Financial Studies 34 (7), 3226-3264, 2021 | 213 | 2021 |
| 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 | 180 | 2023 |
| Neural models for documents with metadata D Card, C Tan, NA Smith arXiv preprint arXiv:1705.09296, 2017 | 167* | 2017 |
| 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 | 146 | 2013 |
| "Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans V Lai, H Liu, C Tan arXiv preprint arXiv:2001.05871, 2020 | 145 | 2020 |
| 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 | 140 | 2010 |
| Active example selection for in-context learning Y Zhang, S Feng, C Tan arXiv preprint arXiv:2211.04486, 2022 | 131 | 2022 |
| Dynamic entity representations in neural language models Y Ji, C Tan, S Martschat, Y Choi, NA Smith arXiv preprint arXiv:1708.00781, 2017 | 126 | 2017 |
| 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 | 124 | 2021 |
| 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 | 121 | 2022 |
| 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 | 117 | 2015 |
| 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 | 113 | 2021 |
Lillian LeeProfessor of Computer Science, Cornell University. Pls see my homepg for data+updates+slides+etcVerified email at cs.cornell.edu
