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An Explanation of In-context Learning as Implicit Bayesian Inference


Nov 14, 2021
Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma


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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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Just Train Twice: Improving Group Robustness without Training Group Information


Jul 19, 2021
Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn

* International Conference on Machine Learning (ICML), 2021 

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Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization


Jul 09, 2021
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt


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Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming


Nov 03, 2020
Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy Liang, Pushmeet Kohli


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Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning


Aug 06, 2020
Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

* Project web page at https://ezliu.github.io/dream 

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The Pitfalls of Simplicity Bias in Neural Networks


Jun 13, 2020
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli


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An Investigation of Why Overparameterization Exacerbates Spurious Correlations


May 09, 2020
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang


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Robust Encodings: A Framework for Combating Adversarial Typos


May 04, 2020
Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang

* ACL 2020 

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DROCC: Deep Robust One-Class Classification


Feb 28, 2020
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain


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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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Certified Robustness to Adversarial Word Substitutions


Sep 03, 2019
Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang

* EMNLP 2019 

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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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Unlabeled Data Improves Adversarial Robustness


Jun 10, 2019
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi


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Maximum Weighted Loss Discrepancy


Jun 08, 2019
Fereshte Khani, Aditi Raghunathan, Percy Liang

* ICLR 2019 Workshop. Safe Machine Learning: Specification, Robustness, and Assurance 

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Semidefinite relaxations for certifying robustness to adversarial examples


Nov 02, 2018
Aditi Raghunathan, Jacob Steinhardt, Percy Liang

* To appear at NIPS 2018 

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Certified Defenses against Adversarial Examples


Jan 29, 2018
Aditi Raghunathan, Jacob Steinhardt, Percy Liang


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Estimating the unseen from multiple populations


Jul 12, 2017
Aditi Raghunathan, Greg Valiant, James Zou

* 13 pages, 3 figures, appearing at the International Conference on Machine Learning 2017 (ICML 2017) 

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Learning Mixture of Gaussians with Streaming Data


Jul 08, 2017
Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain

* 20 pages, 1 figure 

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Estimation from Indirect Supervision with Linear Moments


Aug 10, 2016
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang

* 12 pages, 7 figures, extended and updated version of our paper appearing in ICML 2016 

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Probabilistic Dependency Networks for Prediction and Diagnostics


Aug 13, 2015
Narayanan U. Edakunni, Aditi Raghunathan, Abhishek Tripathi, John Handley, Fredric Roulland

* Presented at the Transportation Research Board Annual Meeting 2014 

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A Reinforcement Learning Approach to Online Learning of Decision Trees


Jul 24, 2015
Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran


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