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Research

Vowpal Wabbit was created in response to an internal contest at Yahoo! Research in 2007. It performed well and developed into an active open source project — the first of its kind for online interactive learning. The momentum from open collaboration helped Vowpal Wabbit develop structurally with a reduction stack, improve performance with parallel learning, and added unique interactive learning capabilities.


The Vowpal Wabbit open source project aspires to enable anyone in the world to perform interactive online learning. With ongoing contributions and open research collaboration, the project continues to create new interactive machine learning approaches that expand the possibilities of what people and machines achieve together.

Publications

  1. Bietti, Alberto and Agarwal, Alekh and Langford, John, A Contextual Bandit Bake-off (2018)
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  3. Alekh Agarwal and Sarah Bird and Markus Cozowicz and Luong Hoang and John Langford and Stephen Lee and Jiaji Li and Dan Melamed and Gal Oshri and Oswaldo Ribas and Siddhartha Sen and Alex Slivkins, A Multiworld Testing Decision Service (2016)
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  5. Adith Swaminathan and Akshay Krishnamurthy and Alekh Agarwal and Miroslav Dudı́k and John Langford and Damien Jose and Imed Zitouni, Off-policy evaluation for slate recommendation (2016)
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  6. Ian Osband and Benjamin Van Roy, Bootstrapped Thompson Sampling and Deep Exploration (2015)
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  7. Dean Eckles and Maurits Kaptein, Thompson sampling with the online bootstrap (2014)
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  8. Alekh Agarwal and Daniel J. Hsu and Satyen Kale and John Langford and Lihong Li and Robert E. Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014)
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  9. Alekh Agarwal and Olivier Chapelle and Miroslav Dudı́k and John Langford, A Reliable Effective Terascale Linear Learning System (2011)
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  10. Karampatziakis, Nikos and Langford, John, Online Importance Weight Aware Updates (2011)
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  11. Miroslav Dudı́k and John Langford and Lihong Li, Doubly Robust Policy Evaluation and Learning (2011)
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  12. Lihong Li and Wei Chu and John Langford and Robert E. Schapire, A Contextual-Bandit Approach to Personalized News Article Recommendation (2010)
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  13. Shi, Qinfeng and Petterson, James and Dror, Gideon and Langford, John and Smola, Alex and Vishwanathan, S.V.N., Hash Kernels for Structured Data (2009)
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  14. Kilian Q. Weinberger and Anirban Dasgupta and Josh Attenberg and John Langford and Alexander J. Smola, Feature Hashing for Large Scale Multitask Learning (2009)
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