Christopher Ré

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Ce ZhangTogether AI; University of ChicagoVerified email at together.xyz
Atri RudraKatherine Johnson Chair in AI, Professor, CSE, University at BuffaloVerified email at buffalo.edu
Sen WuComputer Science, Stanford UniversityVerified email at cs.stanford.edu
Feng NiuUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Kunle OlukotunCadence Design Systems Professor of Computer Science, Stanford UniversityVerified email at stanford.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Michael CafarellaPrincipal Research Scientist, MIT CSAILVerified email at umich.edu
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinVerified email at cs.wisc.edu
Stephen WrightDepartment of Computer Sciences and Wisconsin Institute for Discovery, University of WisconsinVerified email at cs.wisc.edu
Arun KumarUniversity of California, San DiegoVerified email at eng.ucsd.edu
Jaeho ShinEvidently, Inc.Verified email at cs.stanford.edu
Manas JoglekarOpenAIVerified email at openai.com
Victor BittorfFacebookVerified email at fb.com
Ioannis (Yannis) MitliagkasAssociate Professor at Mila, University of MontréalVerified email at iro.umontreal.ca
Magdalena BalazinskaUniversity of WashingtonVerified email at cs.washington.edu
Dolan AntenucciUniversity of MichiganVerified email at umich.edu
Joseph HellersteinJim Gray Professor of the Graduate School, UC BerkeleyVerified email at berkeley.edu
Srikrishna SridharAppleVerified email at tetra.ai
Ji LiuMetaVerified email at meta.com
Xixuan (Aaron) Feng 冯熙铉Software Engineer and Researcher at Megagon Labs

Christopher Ré

Computer Science, Stanford University

Verified email at cs.stanford.edu - Homepage

machine learning artificial intelligence machine learning systems data management AI systems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021	4844	2021
Hogwild: A lock-free approach to parallelizing stochastic gradient descent F Niu, B Recht, C Ré, S Wright Advances in Neural Information Processing Systems, 693-701, 2011	2830*	2011
Flashattention: Fast and memory-efficient exact attention with io-awareness T Dao, D Fu, S Ermon, A Rudra, C Ré Advances in Neural Information Processing Systems 35, 16344-16359, 2022	1762	2022
Efficiently modeling long sequences with structured state spaces A Gu, K Goel, C Ré arXiv preprint arXiv:2111.00396, 2021	1675	2021
Snorkel: Rapid training data creation with weak supervision A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré Proceedings of the VLDB endowment. International conference on very large …, 2017	1454	2017
Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022	1201	2022
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features KH Yu, C Zhang, GJ Berry, RB Altman, C Ré, DL Rubin, M Snyder Nature communications 7 (1), 12474, 2016	1054	2016
Data programming: Creating large training sets, quickly AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré Advances in neural information processing systems 29, 2016	894	2016
Incremental knowledge base construction using DeepDive C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang The VLDB Journal 26, 81-105, 2017	872*	2017
Hyperbolic graph convolutional neural networks I Chami, Z Ying, C Ré, J Leskovec Advances in neural information processing systems 32, 2019	787	2019
Probabilistic databases D Suciu, D Olteanu, C Ré, C Koch Springer Nature, 2022	686	2022
Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017	576	2017
The MADlib analytics library or MAD skills, the SQL J Hellerstein, C Ré, F Schoppmann, DZ Wang, E Fratkin, A Gorajek, ... arXiv preprint arXiv:1208.4165, 2012	551	2012
Combining recurrent, convolutional, and continuous-time models with linear state space layers A Gu, I Johnson, K Goel, K Saab, T Dao, A Rudra, C Ré Advances in neural information processing systems 34, 572-585, 2021	542	2021
Representation tradeoffs for hyperbolic embeddings F Sala, C De Sa, A Gu, C Ré International conference on machine learning, 4460-4469, 2018	509	2018
Hippo: Recurrent memory with optimal polynomial projections A Gu, T Dao, S Ermon, A Rudra, C Ré Advances in neural information processing systems 33, 1474-1487, 2020	499	2020
Efficient top-k query evaluation on probabilistic data C Re, N Dalvi, D Suciu 2007 IEEE 23rd International Conference on Data Engineering, 886-895, 2006	493	2006
Low-dimensional hyperbolic knowledge graph embeddings I Chami, A Wolf, DC Juan, F Sala, S Ravi, C Ré arXiv preprint arXiv:2005.00545, 2020	478	2020
An asynchronous parallel stochastic coordinate descent algorithm J Liu, S Wright, C Ré, V Bittorf, S Sridhar International Conference on Machine Learning, 469-477, 2014	452	2014
Hungry hungry hippos: Towards language modeling with state space models DY Fu, T Dao, KK Saab, AW Thomas, A Rudra, C Ré arXiv preprint arXiv:2212.14052, 2022	445	2022

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