Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
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Updated
Dec 11, 2019 - Python
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
🌾 OAT: Online AlignmenT for LLMs
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Thompson Sampling Tutorial
All codes, both created and optimized for best results from the SuperDataScience Course
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Bandit algorithms
pyrff: Python implementation of random fourier feature approximations for gaussian processes
Offline evaluation of multi-armed bandit algorithms
Bayesian Optimization for Categorical and Continuous Inputs
Study of the paper 'Neural Thompson Sampling' published in October 2020
A Julia Package for providing Multi Armed Bandit Experiments
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
A curated list on papers about combinatorial multi-armed bandit problems.
Thompson Sampling based Monte Carlo Tree Search for MDPs and POMDPs
Author's implementation of the paper Correlated Age-of-Information Bandits.
Optimizing the best Ads using Reinforcement learning Algorithms such as Thompson Sampling and Upper Confidence Bound.
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