Contextual Multi-Armed Bandit Reward Tracker & Model Trainer
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Updated
May 21, 2024 - Python
Contextual Multi-Armed Bandit Reward Tracker & Model Trainer
Easily Score & Rank JSON-Encodable Objects with ML
The GitHub repository for "Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo", AISTATS 2024.
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Repository tugas akhir tentang Multi-Armed Bandit
🦾🤖 Visual and interactive simulator of multi-armed bandit problem.
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on
A Novel Multi-Arm Bandit Optimization Implementation using reinforcement learning in Python for selecting Notifications.
Experiments for paper "Bayesian Linear Bandits for Large-Scale Recommender Systems"
Multi-Armed Bandit method of accurately estimating the largest parameter out of a set of candidates.
Simple A/B testing library for Clojure
Easy-to-use library for multi-armed bandit problems.
This repository is for a Decision Making Aarhus University Course assignment, focusing on using Multi-Armed Bandit algorithms, specifically the epsilon-greedy algorithm, for optimizing click-through rates in digital advertising by balancing the exploration of new ads and the exploitation of successful ones.
A classic reinforcement learning problem.
MetaHierTS is a novel recommendation system algorithm aimed at enhancing user experiences in online marketing. This algorithm focuses on leveraging metadata and similarities between tasks to optimize decision-making in a multi-task Multi-Armed Bandit (MAB) environment.
Experiments for paper "Online Learning with Costly Features in Non-stationary Environments"
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
Prof. Jungmin So - spring '23
This repository contains an End to End Real time 🕰️ Machine Learning Pipeline to predict star ⭐️ rating of product reviews. This project uses AWS Sagemaker, Kinesis, Lambda, S3, Redshift, Athena, and Step functions. Deployment of multiple models for AB testing and Bandit testing is also included.
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