vowpalwabbit Goto Github PK

agenda

autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

ccb-experiments

coba

Contextual bandit benchmarking

data-science

docker-images

Docker images used for continuous integration

docs

Genenerated documentation

estimators

Estimators to perform off-policy evaluation

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.

icml2019

jupyter-notebooks

Vowpal Wabbit examples and tutorials

langchain

⚡ Building applications with LLMs through composability ⚡

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.

neurips2019

py-vowpal-wabbit-next

Experimental new Python bindings for the VowpalWabbit library

reinforcement_learning

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

rl_chain

slates-experiments

slope-experiments

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.

vowpalwabbit.github.io

Official website of Vowpal Wabbit

workshop