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Large Language Models Can Be Strong Differentially Private Learners


Oct 12, 2021
Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto

* 29 pages 

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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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Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis


Jun 23, 2021
Zhongliang Li, Zhihao Jin, Xuechen Li, Linlin Shen


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Efficient and Accurate Gradients for Neural SDEs


Jun 15, 2021
Patrick Kidger, James Foster, Xuechen Li, Terry Lyons

* Submitted to NeurIPS 2021 

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Learning to Extend Program Graphs to Work-in-Progress Code


May 28, 2021
Xuechen Li, Chris J. Maddison, Daniel Tarlow


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Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations


Feb 12, 2021
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud


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Neural SDEs as Infinite-Dimensional GANs


Feb 06, 2021
Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons


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When Does Preconditioning Help or Hurt Generalization?


Jul 02, 2020
Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

* 38 pages 

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Scalable Gradients for Stochastic Differential Equations


Feb 24, 2020
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

* AISTATS 2020; 25 pages, 6 figures in main text; fixed various typos 

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Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond


Jun 19, 2019
Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu

* 55 pages 

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A Novel Method for the Absolute Pose Problem with Pairwise Constraints


Mar 28, 2019
Yinlong Liu, Xuechen Li, Manning Wang, Guang Chen, Zhijian Song, Alois Knoll

* 10 pages, 7figures 

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Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition


Jan 05, 2019
Xuechen Li, Yinlong Liu, Yiru Wang, Chen Wang, Manning Wang, Zhijian Song

* 17pages, 16 figures and 6 tables 

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Isolating Sources of Disentanglement in Variational Autoencoders


Oct 22, 2018
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud

* Added more experiments and improved clarity 

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Inference Suboptimality in Variational Autoencoders


May 27, 2018
Chris Cremer, Xuechen Li, David Duvenaud

* ICML 

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