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No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets


Sep 12, 2021
Fahim Tajwar, Ananya Kumar, Sang Michael Xie, Percy Liang

* ICML Workshop on Uncertainty & Robustness in Deep Learning, 2021 

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On the Opportunities and Risks of Foundation Models


Aug 18, 2021
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

* Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) 

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Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning


Jun 17, 2021
Colin Wei, Sang Michael Xie, Tengyu Ma


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In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness


Dec 16, 2020
Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang


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WILDS: A Benchmark of in-the-Wild Distribution Shifts


Dec 14, 2020
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang


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Simplifying Models with Unlabeled Output Data


Jun 29, 2020
Sang Michael Xie, Tengyu Ma, Percy Liang


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Understanding and Mitigating the Tradeoff Between Robustness and Accuracy


Feb 25, 2020
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang


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Adversarial Training Can Hurt Generalization


Jun 14, 2019
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang


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Differentiable Subset Sampling


Jan 29, 2019
Sang Michael Xie, Stefano Ermon


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Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance


May 26, 2018
Neal Jean, Sang Michael Xie, Stefano Ermon


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