Implementation of provably Rawlsian fair ML algorithms for contextual bandits.
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Updated
May 10, 2017 - Jupyter Notebook
Implementation of provably Rawlsian fair ML algorithms for contextual bandits.
Code accompanying the paper "Learning Permutations with Sinkhorn Policy Gradient"
Blocks World -- Simulator, Code, and Models (Misra et al. EMNLP 2017)
Code to trade the financial markets using Contextual Bandits
Awesome list about anything bandit problems
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
Repo for course CSC2558: "Intelligent Adaptive Interventions" project in nonstationary contextual bandits.
Experiment results using MAB algorithms in Yahoo! Front Page Today Module User Click Log dataset
WIP: A library and AWS sdk for non-contextual and contextual Multi-Armed-Bandit (MAB) algorithms for multiple use cases
Code for our ICDMW 2018 paper: "Contextual Bandit with Adaptive Feature Extraction".
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
Code for our AJCAI 2020 paper: "Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward".
Bandits codes contributed by Louie Hoang at MSR.
OCaml bindings to vowpal wabbit
The Contextual Meta-Bandit (CMB) can be used to select models using the context with online learning based on Reiforcement Learning problem. It's can be used for recommender system ensemble, A/B test, and other dynamic model selector problem.
Code of the NeuralBandit paper
Predicting the outcome of League of Legends E-Sports matches using reinforcement learning contextual bandits.
A lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.
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