FLEXGP.CSAIL.MIT.EDU

The FlexGP Project

In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains. And a tutorial to running them on multiple examples here. At CSAIL, MIT.

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In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains. And a tutorial to running them on multiple examples here. At CSAIL, MIT.

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The FlexGP Project | flexgp.csail.mit.edu Reviews

https://flexgp.csail.mit.edu

In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains. And a tutorial to running them on multiple examples here. At CSAIL, MIT.

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flexgp.csail.mit.edu

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The FlexGP Project

http://flexgp.csail.mit.edu/index.php

In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains. And a tutorial to running them on multiple examples here. At CSAIL, MIT.

2

The FlexGP Project

http://flexgp.csail.mit.edu/people.php

Owen Derby (MEng, 2013). Dylan Sherry (MEng, 2013). FlexGP is a project of ALFA. At CSAIL, MIT.

3

The FlexGP Project

http://flexgp.csail.mit.edu/publications.php

Building predictive models via feature synthesis. Ignacio Arnaldo, Kalyan Veeramachaneni, Una-May O'Reilly, GECCO '15, pp TBD. Bring Your Own Learner! A cloud-based, data-parallel commons for machine learning, Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O'Reilly. To appear in IEEE Computational Intelligence Magazine. Special Issue on Computational Intelligence for Cloud Computing (Feb. 2015). Multiple regression genetic programming. Owen Derby, Kalyan Veeramachaneni, and Una-May O'Reilly...

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kalyanv.org

News - Kalyan Veeramachaneni

http://www.kalyanv.org/news.html

6/30 - MIT News coverage on our MOOC dropout prediction work. Coverage in campus technology. Coverage in Information week. A previous blog on edX. Current paper can be accessed here. See the competition website here. See the leaderboard here. 6/12 - 6/18 - Gave invited talks at Citbank HQ- Mumbai, Accenture, XCRI, Aspiring Minds, MIT Club - New Delhi. 6/10 - Gave a talk at CSAIL's annual industry affiliate program about PatternEx - An AI platform for Cybersecurity. Towards a Data Driven Society. 1/20 - T...

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flexgp.csail.mit.edu

The FlexGP Project

In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains. And a tutorial to running them on multiple examples here. At CSAIL, MIT.

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