| 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 | 316 | 2018 |
| “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 | 302 | 2015 |
| "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 | 181* | 2019 |
| 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 | 178* | 2023 |
| 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 | 98 | 2020 |
| 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 | 82 | 2021 |
| Investigating Human+ Machine Complementarity: A Case Study on Recidivism S Tan, J Adebayo, K Inkpen, E Kamar arXiv preprint arXiv:1808.09123, 2018 | 76* | 2018 |
| 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 | 47 | 2020 |
| 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 | 45 | 2019 |
| 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 | 35 | 2020 |
| 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 | 17 | 2023 |
| Using explainable boosting machines (EBMs) to detect common flaws in data Z Chen, S Tan, H Nori, K Inkpen, Y Lou, R Caruana ECML-PKDD International Workshop and Tutorial on eXplainable Knowledge …, 2021 | 14 | 2021 |
| 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 |
| 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 | 12* | 2022 |
| Interpretable Approaches to Detect Bias in Black-Box Models S Tan Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society …, 2018 | 10 | 2018 |
| MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases R Murthy, L Yang, J Tan, TM Awalgaonkar, Y Zhou, S Heinecke, S Desai, ... arXiv preprint arXiv:2406.10290, 2024 | 7 | 2024 |
| 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 Conference on Digital Experimentation 2021, 2022 | 5 | 2022 |
| A Double Parametric Bootstrap Test for Topic Models S Seto, S Tan, G Hooker, MT Wells NeurIPS 2017 Interpretability Symposium, 2017 | 2 | 2017 |
| Evaluating Cultural and Social Awareness of LLM Web Agents H Qiu, AR Fabbri, D Agarwal, KH Huang, S Tan, N Peng, CS Wu arXiv preprint arXiv:2410.23252, 2024 | 1 | 2024 |
| XForecast: Evaluating Natural Language Explanations for Time Series Forecasting T Aksu, C Liu, A Saha, S Tan, C Xiong, D Sahoo arXiv preprint arXiv:2410.14180, 2024 | 1 | 2024 |
Rich CaruanaMicrosoft Researchmicrosoft.com의 이메일 확인됨