2015 - Fellow of the MacArthur Foundation
2013 - Fellow of Alfred P. Sloan Foundation
His main research concerns Artificial intelligence, Algorithm, Data mining, Machine learning and Theoretical computer science. His studies deal with areas such as Matrix, Stochastic gradient descent and Sublinear function as well as Algorithm. His Stochastic gradient descent research is multidisciplinary, incorporating perspectives in Synchronization, Optimization problem, Scheme and Non-blocking algorithm.
The Data mining study combines topics in areas such as Probabilistic logic, Inference, Statistical model and Leverage. His work in Machine learning tackles topics such as Heuristic which are related to areas like Pipeline. His Theoretical computer science research is multidisciplinary, relying on both Simple, Query language, Semantics, Query optimization and Join.
His primary areas of investigation include Artificial intelligence, Machine learning, Algorithm, Theoretical computer science and Data mining. His Artificial intelligence study frequently draws connections to other fields, such as Pattern recognition. The Discriminative model research Christopher Ré does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Bottleneck, therefore creating a link between diverse domains of science.
His Algorithm research also works with subjects such as
His primary scientific interests are in Artificial intelligence, Machine learning, Algorithm, Training set and Benchmark. His Artificial intelligence research incorporates elements of Natural language processing, Key and Pattern recognition. His research in Key intersects with topics in Labeled data and Software deployment.
Christopher Ré has included themes like Probabilistic logic, Baseline and Heuristics in his Machine learning study. His Algorithm research incorporates themes from Matrix and Recurrent neural network. The various areas that Christopher Ré examines in his Training set study include Structure and Regularization.
Christopher Ré mainly focuses on Artificial intelligence, Machine learning, Training set, Deep learning and Algorithm. His study brings together the fields of Key and Artificial intelligence. His work in the fields of Machine learning, such as Transfer of learning, overlaps with other areas such as Bottleneck.
His Training set research incorporates themes from Health care, Text mining, Structure, Heuristics and Visualization. As a member of one scientific family, Christopher Ré mostly works in the field of Deep learning, focusing on Computation and, on occasion, Trajectory, Quantization, Stochastic gradient descent, Machine translation and Inference. The Algorithm study combines topics in areas such as Matrix, Convolution and Join.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Benjamin Recht;Christopher Re;Stephen Wright;Feng Niu.
neural information processing systems (2011)
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Feng Niu;Benjamin Recht;Christopher Re;Stephen J. Wright.
arXiv: Optimization and Control (2011)
Snorkel: rapid training data creation with weak supervision
Alexander Ratner;Stephen H. Bach;Henry Ehrenberg;Jason Fries.
very large data bases (2017)
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.
Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman.
Nature Communications (2016)
Efficient Top-k Query Evaluation on Probabilistic Data
C. Re;N. Dalvi;D. Suciu.
international conference on data engineering (2007)
The MADlib analytics library: or MAD skills, the SQL
Joseph M. Hellerstein;Christoper Ré;Florian Schoppmann;Daisy Zhe Wang.
very large data bases (2012)
Data Programming: Creating Large Training Sets, Quickly
Alexander J. Ratner;Christopher M. De Sa;Sen Wu;Daniel Selsam.
neural information processing systems (2016)
Parallel stochastic gradient algorithms for large-scale matrix completion
Benjamin Recht;Christopher Ré.
Mathematical Programming Computation (2013)
An asynchronous parallel stochastic coordinate descent algorithm
Ji Liu;Stephen J. Wright;Christopher Ré;Victor Bittorf.
Journal of Machine Learning Research (2015)
HoloClean: holistic data repairs with probabilistic inference
Theodoros Rekatsinas;Xu Chu;Ihab F. Ilyas;Christopher Ré.
very large data bases (2017)
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