Christopher Ré
Christopher Ré
Computer Science, Stanford University
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
Probabilistic databases
D Suciu, D Olteanu, C Ré, C Koch
Springer Nature, 2022
Hyperbolic graph convolutional neural networks
I Chami, Z Ying, C Ré, J Leskovec
Advances in neural information processing systems 32, 2019
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
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
Holoclean: Holistic data repairs with probabilistic inference
T Rekatsinas, X Chu, IF Ilyas, C Ré
arXiv preprint arXiv:1702.00820, 2017
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
Parallel stochastic gradient algorithms for large-scale matrix completion
B Recht, C Ré
Mathematical Programming Computation 5 (2), 201-226, 2013
Tuffy: Scaling up statistical inference in markov logic networks using an rdbms
F Niu, C Ré, AH Doan, J Shavlik
arXiv preprint arXiv:1104.3216, 2011
Learning to compose domain-specific transformations for data augmentation
AJ Ratner, H Ehrenberg, Z Hussain, J Dunnmon, C Ré
Advances in neural information processing systems 30, 2017
Representation tradeoffs for hyperbolic embeddings
F Sala, C De Sa, A Gu, C Ré
International conference on machine learning, 4460-4469, 2018
Dawnbench: An end-to-end deep learning benchmark and competition
C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ...
Training 100 (101), 102, 2017
Worst-case optimal join algorithms
HQ Ngo, E Porat, C Ré, A Rudra
Journal of the ACM (JACM) 65 (3), 1-40, 2018
Low-dimensional hyperbolic knowledge graph embeddings
I Chami, A Wolf, DC Juan, F Sala, S Ravi, C Ré
arXiv preprint arXiv:2005.00545, 2020
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L Oakden-Rayner, J Dunnmon, G Carneiro, C Ré
Proceedings of the ACM conference on health, inference, and learning, 151-159, 2020
The system can't perform the operation now. Try again later.
Articles 1–20