Sarah Tan - RankMe

30 papers A* 5A 1C 2Journal 18Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Enduring Disparities in the Workplace: A Pilot Study in the AI Community. CoRR Yunusa Simpa Abdulsalam, Siobhan Mackenzie Hall, Ana María Quintero-Ossa, William Agnew, Carla Muntean, Sarah Tan, Ashley Heady, Savannah Thais, Jessica Schrouff
2025	—	`conf`	Evaluating Cultural and Social Awareness of LLM Web Agents. NAACL (Findings) Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal, Kung-Hsiang Huang, Sarah Tan, Nanyun Peng, Chien-Sheng Wu
2024	J	`jnl`	Evaluating Cultural and Social Awareness of LLM Web Agents. CoRR Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal, Kung-Hsiang Huang, Sarah Tan, Nanyun Peng, Chien-Sheng Wu
2024	J	`jnl`	How Aligned are Generative Models to Humans in High-Stakes Decision-Making? CoRR Sarah Tan, Keri Mallari, Julius Adebayo, Albert Gordo, Martin T. Wells, Kori Inkpen
2024	J	`jnl`	MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases. CoRR 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
2024	J	`jnl`	XForecast: Evaluating Natural Language Explanations for Time Series Forecasting. CoRR Taha Aksu, Chenghao Liu, Amrita Saha, Sarah Tan, Caiming Xiong, Doyen Sahoo
2023	J	`jnl`	Considerations when learning additive explanations for black-box models. Mach. Learn. Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana
2023	A*	`conf`	Error Discovery By Clustering Influence Embeddings. NeurIPS Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan
2023	J	`jnl`	Error Discovery by Clustering Influence Embeddings. CoRR Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan
2023	—	`conf`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	Practical Policy Optimization with Personalized Experimentation. CoRR Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy
2022	J	`jnl`	Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes. CoRR Leon Yao, Caroline Lo, Israel Nir, Sarah Tan, Ariel Evnine, Adam Lerer, Alex Peysakhovich
2022	A*	`conf`	Interpretable Personalized Experimentation. KDD Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy
2021	J	`jnl`	Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. CoRR Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy
2021	A*	`conf`	How Interpretable and Trustworthy are GAMs? KDD Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana
2021	—	`conf`	Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. PKDD/ECML Workshops (1) Zhi Chen, Sarah Tan, Harsha Nori, Kori Inkpen, Yin Lou, Rich Caruana
2020	A*	`conf`	Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. CHI Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, Ece Kamar
2020	J	`jnl`	Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. CoRR Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, Ece Kamar
2020	J	`jnl`	How Interpretable and Trustworthy are GAMs? CoRR Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana
2020	A	`conf`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2020	—	`conf`	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. FODS Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells
2019	A*	`conf`	Axiomatic Interpretability for Multiclass Additive Models. KDD Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2019	J	`jnl`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2018	C	`conf`	Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
2018	J	`jnl`	Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2018	C	`conf`	Interpretable Approaches to Detect Bias in Black-Box Models. AIES Sarah Tan
2018	J	`jnl`	Investigating Human + Machine Complementarity for Recidivism Predictions. CoRR Sarah Tan, Julius Adebayo, Kori Inkpen, Ece Kamar
2018	J	`jnl`	Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo
2017	J	`jnl`	Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou