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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/evensgn/hyperbo

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HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes

https://github.com/ewendawi/llamea-bo

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LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms

https://github.com/experimental-design/bofire

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BoFire: Bayesian Optimization Framework Intended for Real Experiments

https://github.com/facebookresearch/ContextualBO

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Bayesian Optimization with High-Dimensional Outputs

https://github.com/facebookresearch/LaMCTS

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Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

https://github.com/facebookresearch/alebo

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Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

https://github.com/facebookresearch/bo_pr

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Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

https://github.com/facebookresearch/heterogeneous_botl

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Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces

https://github.com/facebookresearch/preference-exploration

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Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes

https://github.com/facebookresearch/qeubo

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qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization

https://github.com/facebookresearch/robust_mobo

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Robust Multi-Objective Bayesian Optimization Under Input Noise

https://github.com/facebookresearch/sparsebo

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Sparse Bayesian Optimization

https://github.com/facebookresearch/sustainableconcrete

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Sustainable Concrete via Bayesian Optimization

https://github.com/fanhanwei/bocr

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Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning

https://github.com/fanhanwei/fnn_mfrl_archdse

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Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration

https://github.com/faosorios/heavy

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Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization

https://github.com/feiliu36/eoh

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Evolve Cost-aware Acquisition Functions Using Large Language Models

https://github.com/fernandezdaniel/Spearmint

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Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints

https://github.com/fernandezdaniel/alphaES

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Alpha Entropy Search for New Information-based Bayesian Optimization

https://github.com/fjwillemsen/BayesianOptimization-autotuning

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Bayesian Optimization for auto-tuning GPU kernels

https://github.com/fmohr/naiveautoml

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Naive Automated Machine Learning

https://github.com/fraunhofer-iais/MinMaxOpt

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Bayesian Optimization for Min Max Optimization

https://github.com/fraunhofer-iais/Robust-Entropy-Search

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Robust Entropy Search for Safe Efficient Bayesian Optimization

https://github.com/frazier-lab/partial_kgfn

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Bayesian Optimization of Function Networks with Partial Evaluations

https://github.com/frmicheli/WDRBO

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Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context

https://github.com/fsahli/purkinje-learning

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Probabilistic learning of the Purkinje network from the electrocardiogram

https://github.com/fsk-lab/botier

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BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives

https://github.com/fsoft-aic/lassobo

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High Dimensional Bayesian Optimization using Lasso Variable Selection

https://github.com/fsoft-aic/rpm-bo

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High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces

https://github.com/funalab/pipebo

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Asynchronous Batch Bayesian Optimization with Pipelining Evaluations for Experimental Resource$\unicode{x2013}$constrained Conditions

https://github.com/gkwt/rbo

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Ranking over Regression for Bayesian Optimization and Molecule Selection

https://github.com/gloaming2dawn/aibo

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Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization

https://github.com/google-research/google-research/tree/master/meta_reward_learning

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Learning to Generalize from Sparse and Underspecified Rewards

https://github.com/google-research/gpax

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Pre-trained Gaussian Processes for Bayesian Optimization

https://github.com/google-research/hyperbo

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Pre-trained Gaussian Processes for Bayesian Optimization

https://github.com/google-research/optformer

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Predicting from Strings: Language Model Embeddings for Bayesian Optimization

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