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Awesome Vowpal Wabbit
Yury Kashnitskiy edited this page Nov 4, 2019
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This page contains a curated list of examples, research, videos, tutorials, and blogs about Vowpal Wabbit usecases.
- Yahoo used Vowpal Wabbit for personalized spam filtering.
- eHarmony uses Vowpal Wabbit for recommendation. Code. Presentation by Zhen.
- Microsoft implemented Vowpal Wabbit as part of Azure ML.
- Nanigans uses Vowpal Wabbit for all of their ads portfolio optimization problems and train several thousand models per day.
- An official list of tutorials from workshops introducing new features.
- An official list of examples / use cases.
- Predicting CTR with Online Machine Learning by MLWave. Uses Criteo dataset.
- Go non-linear with Vowpal Wabbit by FastML.
- Vowpal Wabbit tutorial for the Uninitiated on Convex Optimized.
- Solving NLP problems with Vowpal Wabbit by Hal Daumé III.
- Machine Learning meets ketosis: how to effectively lose weight by Ariel Faigon.
- Vowpal Wabbit tutorial: blazingly fast learning - tutorial in a form of a Kaggle Kernel by Yury Kashnitsky
- ...
- Enhancing performance of VW for text classification by Zachary Deane-Mayer.
- Tips for using VW for sentiment analysis by MLWave.
- Lessons Learned from the Hunt for Prohibited Content on Kaggle.
- Profiling Top Kagglers: Gilberto Titericz, New #1 in the World.
- One Louder by Paul Mineiro on the neural networks functionality.
- ...
- Walmart Recruiting II: Sales in Stormy Weather. Threecourse used Vowpal Wabbit. First Place Entry Description. Code.
- Guocong Song won 3rd place in the Criteo Ad Click Prediction Challenge. Code.
- 5th place and 3rd place in the KDD-cup 2014: Predicting Excitement at DonorsChoose.org used VW on the text features.
- 1st place in Allstate Claims Severity Competition used VW as part of ensemble. Description.
- ...
- Vowpal Wabbit Workshop
- Technical Tricks of Vowpal Wabbit
- Winning Kaggle 101 by Dmitry Larko.
- Vowpal Wabbit: Fast Learning on Big Data by Ran Bi.
- Vowpal Wabbit, a Machine Learning System. John Langford's talk at QCon.
- ...
- Vowpal Wabbit is implemented in the Multiworld Testing Decision Service.
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- Home
- First Steps
- Input
- Command line arguments
- Model saving and loading
- Controlling VW's output
- Audit
- Algorithm details
- Awesome Vowpal Wabbit
- Learning algorithm
- Learning to Search subsystem
- Loss functions
- What is a learner?
- Docker image
- Model merging
- Evaluation of exploration algorithms
- Reductions
- Contextual Bandit algorithms
- Contextual Bandit Exploration with SquareCB
- Contextual Bandit Zeroth Order Optimization
- Conditional Contextual Bandit
- Slates
- CATS, CATS-pdf for Continuous Actions
- Automl
- Epsilon Decay
- Warm starting contextual bandits
- Efficient Second Order Online Learning
- Latent Dirichlet Allocation
- VW Reductions Workflows
- Interaction Grounded Learning
- CB with Large Action Spaces
- CB with Graph Feedback
- FreeGrad
- Marginal
- Active Learning
- Eigen Memory Trees (EMT)
- Element-wise interaction
- Bindings
-
Examples
- Logged Contextual Bandit example
- One Against All (oaa) multi class example
- Weighted All Pairs (wap) multi class example
- Cost Sensitive One Against All (csoaa) multi class example
- Multiclass classification
- Error Correcting Tournament (ect) multi class example
- Malicious URL example
- Daemon example
- Matrix factorization example
- Rcv1 example
- Truncated gradient descent example
- Scripts
- Implement your own joint prediction model
- Predicting probabilities
- murmur2 vs murmur3
- Weight vector
- Matching Label and Prediction Types Between Reductions
- Zhen's Presentation Slides on enhancements to vw
- EZExample Archive
- Design Documents
- Contribute: