Sarah Tan

According to our database, Sarah Tan authored at least 23 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Evaluating Cultural and Social Awareness of LLM Web Agents.

Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal, Kung-Hsiang Huang, Sarah Tan, Nanyun Peng, Chien-Sheng Wu

CoRR, 2024

How Aligned are Generative Models to Humans in High-Stakes Decision-Making?

Sarah Tan, Keri Mallari, Julius Adebayo, Albert Gordo, Martin T. Wells, Kori Inkpen

CoRR, 2024

XForecast: Evaluating Natural Language Explanations for Time Series Forecasting.

Taha Aksu, Chenghao Liu, Amrita Saha, Sarah Tan, Caiming Xiong, Doyen Sahoo

CoRR, 2024

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases.

Rithesh Murthy, Liangwei Yang, Juntao Tan, Tulika Manoj Awalgaonkar, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, Jianguo Zhang, Zhiwei Liu, Shirley Kokane, Zuxin Liu, Ming Zhu, Huan Wang, Caiming Xiong, Silvio Savarese

CoRR, 2024

2023

Considerations when learning additive explanations for black-box models.

Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana

Mach. Learn., September, 2023

Practical Policy Optimization with Personalized Experimentation.

Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy

CoRR, 2023

Error Discovery By Clustering Influence Embeddings.

Fulton Wang, Julius Adebayo, Sarah Tan, Diego García-Olano, Narine Kokhlikyan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana

Proceedings of the Conference on Health, Inference, and Learning, 2023

2022

Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes.

Leon Yao, Caroline Lo, Israel Nir, Sarah Tan, Ariel Evnine, Adam Lerer, Alex Peysakhovich

CoRR, 2022

Interpretable Personalized Experimentation.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

CoRR, 2021

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data.

Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

How Interpretable and Trustworthy are GAMs?

Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable.

Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells

Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism.

Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, Ece Kamar

Proceedings of the CHI '20: CHI Conference on Human Factors in Computing Systems, 2020

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models.

Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Axiomatic Interpretability for Multiclass Additive Models.

Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2018

Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models.

Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

CoRR, 2018

Investigating Human + Machine Complementarity for Recidivism Predictions.

Sarah Tan, Julius Adebayo, Kori Inkpen, Ece Kamar

CoRR, 2018

Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo

CoRR, 2018

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

Interpretable Approaches to Detect Bias in Black-Box Models.

Sarah Tan

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017

Detecting Bias in Black-Box Models Using Transparent Model Distillation.

Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

CoRR, 2017