Sarah Tan
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“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
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
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
"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
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
Investigating Human+ Machine Complementarity: A Case Study on Recidivism
S Tan, J Adebayo, K Inkpen, E Kamar
arXiv preprint arXiv:1808.09123, 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
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
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
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
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
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
Interpretable Approaches to Detect Bias in Black-Box Models
S Tan
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society …, 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
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
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
A Double Parametric Bootstrap Test for Topic Models
S Seto, S Tan, G Hooker, MT Wells
NeurIPS 2017 Interpretability Symposium, 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
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
Probabilistic Matching: Incorporating Uncertainty to Correct for Selection Bias
HF Tan, GJ Hooker, MT Wells
NeurIPS 2016 Causal Inference Workshop, 2016
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