| “No fracking way!” Documentary film, discursive opportunity, and local opposition against hydraulic fracturing in the United States, 2010 to 2013 IB Vasi, ET Walker, JS Johnson, HF Tan American Sociological Review 80 (5), 934-959, 2015 | 284 | 2015 |
| Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S Tan, R Caruana, G Hooker, Y Lou Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018 | 283 | 2018 |
| Considerations When Learning Additive Explanations for Black-Box Models S Tan, G Hooker, P Koch, A Gordo, R Caruana Machine Learning 112, 3333 - 3359, 2023 | 163* | 2023 |
| "Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations Y Zhang, K Song, Y Sun, S Tan, M Udell ICML 2019 AI for Social Good Workshop, 2019 | 88 | 2019 |
| Tree space prototypes: Another look at making tree ensembles interpretable S Tan, M Soloviev, G Hooker, MT Wells Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference, 23-34, 2020 | 85 | 2020 |
| Investigating Human+ Machine Complementarity: A Case Study on Recidivism S Tan, J Adebayo, K Inkpen, E Kamar arXiv preprint arXiv:1808.09123, 2018 | 72* | 2018 |
| How Interpretable and Trustworthy are GAMs? CH Chang, S Tan, B Lengerich, A Goldenberg, R Caruana Proceedings of the 27th ACM SIGKDD International Conference on Knowledge …, 2021 | 66 | 2021 |
| Axiomatic Interpretability for Multiclass Additive Models X Zhang, S Tan, P Koch, Y Lou, U Chajewska, R Caruana Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 44 | 2019 |
| Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models B Lengerich, S Tan, CH Chang, G Hooker, R Caruana International Conference on Artificial Intelligence and Statistics, 2402-2412, 2020 | 34 | 2020 |
| Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism K Mallari, K Inkpen, P Johns, S Tan, D Ramesh, E Kamar Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020 | 27 | 2020 |
| A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City S Tan, S Makela, D Heller, K Konty, S Balter, T Zheng, JH Stark Epidemics 23 (June 2018), 96-109, 2018 | 14 | 2018 |
| Using explainable boosting machines (EBMs) to detect common flaws in data Z Chen, S Tan, H Nori, K Inkpen, Y Lou, R Caruana Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 11 | 2021 |
| Interpretable Approaches to Detect Bias in Black-Box Models S Tan Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society …, 2018 | 9 | 2018 |
| Interpretable Personalized Experimentation H Wu, S Tan, W Li, M Garrard, A Obeng, D Dimmery, S Singh, H Wang, ... Proceedings of the 28th ACM SIGKDD International Conference on Knowledge …, 2022 | 8* | 2022 |
| Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? Z Chen, S Tan, U Chajewska, C Rudin, R Caruna Conference on Health, Inference, and Learning, 86-99, 2023 | 7 | 2023 |
| Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes L Yao, C Lo, I Nir, S Tan, A Evnine, A Lerer, A Peysakhovich arXiv preprint arXiv:2206.04907, 2022 | 4 | 2022 |
| A Double Parametric Bootstrap Test for Topic Models S Seto, S Tan, G Hooker, MT Wells NeurIPS 2017 Interpretability Symposium, 2017 | 2 | 2017 |
| Error Discovery by Clustering Influence Embeddings F Wang, J Adebayo, S Tan, D Garcia-Olano, N Kokhlikyan Advances in Neural Information Processing Systems 36, 2023 | 1 | 2023 |
| Practical Policy Optimization with Personalized Experimentation M Garrard, H Wang, B Letham, S Singh, A Kazerouni, S Tan, Z Wang, ... NeurIPS 2021 Causal Inference Challenges in Sequential Decision Making Workshop, 2023 | | 2023 |
| Probabilistic Matching: Incorporating Uncertainty to Correct for Selection Bias HF Tan, GJ Hooker, MT Wells NeurIPS 2016 Causal Inference Workshop, 2016 | | 2016 |
Rich CaruanaMicrosoft Research在 microsoft.com 的电子邮件经过验证
