Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields
The Thesis Review
1 hour 18 minutes 1 second
3 years ago
[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields
Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs.
Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis.
- Episode notes: https://cs.nyu.edu/~welleck/episode42.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
The Thesis Review
Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.
Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.
- Episode notes: www.wellecks.com/thesisreview/episode48.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Follow Tianqi Chen on Twitter (@tqchenml)
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
