Vowpal Wabbit, A Machine Learning System
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Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/29g799t. John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications. He goes into detail about the use in the context of a decision service for personalized news recommendation. Filmed at qconnewyork.com. John Langford is a machine learning research scientist. He works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He was the program co-chair for the 2012 International Conference on Machine Learning.
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Vowpal Wabbit, A Machine Learning System
- 2. InfoQ.com: News & Community Site • 750,000 unique visitors/month • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • News 15-20 / week • Articles 3-4 / week • Presentations (videos) 12-15 / week • Interviews 2-3 / week • Books 1 / month Watch the video with slide synchronization on InfoQ.com! https://www.infoq.com/presentations/ vowpal-wabbit
- 3. Presented at QCon New York www.qconnewyork.com Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide
- 4. 1 1 5 4 3 7 5 3 5 3 5 5 9 0 6 3 5 2 0 0
- 5. Run ML algorithm
- 6. Gather your data Label your data Run ML algorithm Embed into larger system Can we make it easier?
- 8. Ex: Which news? Repeatedly: 1. Observe features of user+articles 2. Choose a news article. 3. Observe click-or-not Goal: Maximize fraction of clicks
- 9. Contextual Bandits Repeatedly: 1. Observe features 2. Choose action ∈ 3. Observe reward Goal: Minimize regret to a set of policies.
- 11. Policy
- 14. , , , ∗ : → = 1 ( = ) ( , , , )
- 15. ( , ⃗) ( ) = ( ) ( , ⃗) ∼ ⃗ ( ) = ( = ) = ( )
- 17. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , )
- 18. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , ) Explore
- 19. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , ) Log
- 20. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , ) Learn
- 21. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , ) Deploy
- 22. Client Library Or Web API Join Server Online Learning Offline Learning Policy App context decision reward ( , , , ) , ( , , , ) Offline Learn Data
- 25. … > 25% increase in clicks (without much tuning)
- 33. Watch the video with slide synchronization on InfoQ.com! https://www.infoq.com/presentations/vowpal- wabbit