VowpalWabbit/icml2019

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Created almost 5 years ago
Updated almost 5 years ago

VowpalWabbit/icml2019

VowpalWabbit

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vowpal_wabbit

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

reinforcement_learning

Interaction-side integration library for Reinforcement Learning loops: Predict, Log, [Learn,] Update

coba

Contextual bandit benchmarking

jupyter-notebooks

Vowpal Wabbit examples and tutorials

Jupyter Notebook

estimators

Estimators to perform off-policy evaluation

py-vowpal-wabbit-next

Experimental new Python bindings for the VowpalWabbit library

docker-images

Docker images used for continuous integration

workshop

Jupyter Notebook

data-science

rl_chain

neurips2019

slates-experiments

Jupyter Notebook

docs

Genenerated documentation

slope-experiments

vowpalwabbit.github.io

Official website of Vowpal Wabbit

learn_to_pick

A python library for online learning in RL loops, specialized for Contextual Bandit scenarios. Choose actions from multiple options, evaluate decisions, and integrate feedback for improved future outcomes. Features versatile scoring, advanced featurization, and configurable learning policies.

Jupyter Notebook

ccb-experiments

feature-broker

Mature software applications are highly componentized and have well-defined API boundaries among components. This structure leads to constraints making in-process inference challenging due to the difficulty of access to features. The FeatureBroker library presents a solution to this problem. This helped with adoption of VW in inference. We believe that this is a pervasive problem. The goal of open sourcing this library is to reduce the developer friction in adopting VW in software stacks that are built with these constraints.