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2020 – today
- 2024
[i16]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:
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases. CoRR abs/2406.10290 (2024)- [i15]Taha Aksu, Chenghao Liu, Amrita Saha, Sarah Tan, Caiming Xiong, Doyen Sahoo:
XForecast: Evaluating Natural Language Explanations for Time Series Forecasting. CoRR abs/2410.14180 (2024) - [i14]Sarah Tan, Keri Mallari, Julius Adebayo, Albert Gordo, Martin T. Wells, Kori Inkpen:
How Aligned are Generative Models to Humans in High-Stakes Decision-Making? CoRR abs/2410.15471 (2024) - [i13]Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal, Kung-Hsiang Huang, Sarah Tan, Nanyun Peng, Chien-Sheng Wu:
Evaluating Cultural and Social Awareness of LLM Web Agents. CoRR abs/2410.23252 (2024) - 2023
- [j1]Sarah Tan
, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana:
Considerations when learning additive explanations for black-box models. Mach. Learn. 112(9): 3333-3359 (2023) - [c11]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - [c10]Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan:
Error Discovery By Clustering Influence Embeddings. NeurIPS 2023 - [i12]Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy:
Practical Policy Optimization with Personalized Experimentation. CoRR abs/2303.17648 (2023) - [i11]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - [i10]Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan:
Error Discovery by Clustering Influence Embeddings. CoRR abs/2312.04712 (2023) - 2022
- [c9]Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Interpretable Personalized Experimentation. KDD 2022: 4173-4183 - [i9]Leon Yao, Caroline Lo, Israel Nir, Sarah Tan, Ariel Evnine, Adam Lerer, Alex Peysakhovich:
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes. CoRR abs/2206.04907 (2022) - 2021
- [c8]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? KDD 2021: 95-105 - [c7]Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana:
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) 2021: 534-551 - [i8]Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. CoRR abs/2111.03267 (2021) - 2020
- [c6]Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana:
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS 2020: 2402-2412 - [c5]Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, Ece Kamar:
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. CHI 2020: 1-13 - [c4]Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells:
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. FODS 2020: 23-34 - [i7]Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, Ece Kamar:
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. CoRR abs/2002.01111 (2020) - [i6]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? CoRR abs/2006.06466 (2020)
2010 – 2019
- 2019
- [c3]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Axiomatic Interpretability for Multiclass Additive Models. KDD 2019: 226-234 - [i5]Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana:
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR abs/1911.04974 (2019) - 2018
- [c2]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES 2018: 303-310 - [c1]Sarah Tan:
Interpretable Approaches to Detect Bias in Black-Box Models. AIES 2018: 382-383 - [i4]Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo:
Transparent Model Distillation. CoRR abs/1801.08640 (2018) - [i3]Sarah Tan, Julius Adebayo, Kori Inkpen, Ece Kamar:
Investigating Human + Machine Complementarity for Recidivism Predictions. CoRR abs/1808.09123 (2018) - [i2]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR abs/1810.09092 (2018) - 2017
- [i1]Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou:
Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR abs/1710.06169 (2017)
Coauthor Index
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