Thompson Sampling Tutorial

Contextual bandit algorithm called LinUCB / Linear Upper Confidence Bounds as proposed by Li, Langford and Schapire

Another A/B test library

Privacy-Preserving Bandits (MLSys'20)

Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto

Client that handles the administration of StreamingBandit online, or straight from your desktop. Setup and run streaming (contextual) bandit experiments in your browser.

Network-Oriented Repurposing of Drugs Python Package

Movie Recommendation using Cascading Bandits namely CascadeLinTS and CascadeLinUCB

Adversarial multi-armed bandit algorithms

Reinforcement learning

A/B testing metrics collection with golang

Solutions to the Stanford CS:234 Reinforcement Learning 2022 course assignments.

Research project on automated A/B testing of software by evolutionary bandits.

This presentation contains very precise yet detailed explanation of concepts of a very interesting topic -- Reinforcement Learning.

A small collection of Bandit Algorithms (ETC, E-Greedy, Elimination, UCB, Exp3, LinearUCB, and Thompson Sampling)

Online learning approaches to optimize database join operations in PostgreSQL.