Yahoo! news article recommendation system by linUCB

Multi Armed Bandits implementation using the Yahoo! Front Page Today Module User Click Log Dataset

Predict and recommend the news articles, user is most likely to click in real time.

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

Bandit algorithms

A comprehensive Python library implementing a variety of contextual and non-contextual multi-armed bandit algorithms—including LinUCB, Epsilon-Greedy, Upper Confidence Bound (UCB), Thompson Sampling, KernelUCB, NeuralLinearBandit, and DecisionTreeBandit—designed for reinforcement learning applications

Some visualizations of bandit algorithm outputs.

Reinforcement learning models for warfarin dose online estimation

python implementation of e-Greedy, UCB, LinUCB, LinThompson, and offline evaluator

LinUCB with disjoint linear models

Implementation of the Multi-Armed Bandit where each arm returns continuous numerical rewards. Covers Epsilon-Greedy, UCB1, and Thompson Sampling with detailed explanations.

Recommendation using LinUCB algorithm

Multi Armed Bandits implementation on MIND dataset.

Data Mining course at ETH Zürich.

A collection of implementations of the bandit problem.

RL-based DBMS Buffer Pool Auto-Tuning for Optimal and Economical Memory Utilization. Thus, controls memory footprint of a relational database

Streamline learning using llm_RAG. Platform to allow students prepare for NSMQ science competition.