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Bayesian Optimization

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/QUVA-Lab/HyperSphere

BOCK : Bayesian Optimization with Cylindrical Kernels

https://github.com/S4iz/beta

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/SK-tklab/BayesianOptimization

Max-value Entropy Search for Efficient Bayesian Optimization

https://github.com/SK-tklab/RandomFourierFeatures

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

https://github.com/Selozhd/UngatedRNN

The Statistical Recurrent Unit

https://github.com/SheffieldML/GPyOpt

Batch Bayesian Optimization via Local Penalization

https://github.com/Somya-Patra/Shell_ai

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/VirgiAgl/DAG-GP

Multi-task Causal Learning with Gaussian Processes

https://github.com/ZachZhu7/banana-git

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

https://github.com/ZmeiGorynych/generative_playground

Grammar Variational Autoencoder

https://github.com/abcp4/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/acerbilab/amortized-conditioning-engine

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference

https://github.com/acerbilab/bads

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

https://github.com/acerbilab/pybads

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

https://github.com/alexgcsa/AutoMLC

A Robust Experimental Evaluation of Automated Multi-Label Classification Methods

https://github.com/alexhernandezgarcia/gflownet

Multi-Fidelity Active Learning with GFlowNets

https://github.com/amzn/differential-privacy-bayesian-optimization

Automatic Discovery of Privacy-Utility Pareto Fronts

https://github.com/amzn/emukit

Emulation of physical processes with Emukit

https://github.com/arberzela/EfficientNAS

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

https://github.com/argonne-lcf/active-learning-md

Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide

https://github.com/aspuru-guzik-group/phoenics

PHOENICS: A universal deep Bayesian optimizer

https://github.com/autogluon/autogluon

Model-based Asynchronous Hyperparameter and Neural Architecture Search

https://github.com/automl/EfficientNAS

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

https://github.com/automl/NASLib

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

https://github.com/automl/autoweka

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

https://github.com/automl/ifbo

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

https://github.com/automl/neps

$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization

https://github.com/automl/pybnn

Scalable Bayesian Optimization Using Deep Neural Networks

https://github.com/baggepinnen/hyperopt.jl

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/bayesian-optimization/bayesianoptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/befelix/SafeOpt-robotics

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics

https://github.com/berndbischl/mlrMBO

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

https://github.com/billchan226/poar-srl-4-robot

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/brunzema/truncated-mvn-sampler

On Controller Tuning with Time-Varying Bayesian Optimization

https://github.com/c-bata/benchmark-warm-starting-cmaes

Warm Starting CMA-ES for Hyperparameter Optimization

https://github.com/c-bata/goptuna

Practical Bayesian Optimization of Machine Learning Algorithms

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.