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Charles Sutton is a machine learning researcher who works on probabilistic methods for software engineering, natural language processing, and more. He is an Honorary Fellow at the University of Edinburgh and has published papers on topics such as large language models, code generation, and variational inference.
Charles Sutton is a research scientist at Google who works on deep learning, probabilistic machine learning, and software engineering. He has published many papers on topics such as program synthesis, neural program synthesis, and code analysis. He also maintains a blog with personal and professional insights.
Charles Sutton is a machine learning expert who works on various applications, such as natural language processing, software engineering, and sustainable energy. He has a PhD from the University of Massachusetts Amherst and a postdoc from the University of California Berkeley.
Charles Sutton, Andrew McCallum and Khashayar Rohanimanesh. Journal of Machine Learning Research 8. 2007. (Combination of dynamic Bayesian networks and conditional random fields. Also considers latent-variable model and cascaded training. Journal version of ICML and EMNLP papers below.) [ .pdf | bib | abstract]
Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana: Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD 2018 : 2279-2288
17. Nov. 2010 · A tutorial on CRFs, a probabilistic method for structured prediction, by Charles Sutton and Andrew McCallum. Learn about CRFs applications, inference, parameter estimation, and large scale implementation.
Charles Sutton is a Research Scientist at Google Research. He is interested in a broad range of applications of machine learning, including NLP, analysis of computer systems, software engineering, and program synthesis. His work in software engineering has won an ACM Distinguished Paper Award.
