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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/snu-adsl/DEEP-BO

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DEEP-BO for Hyperparameter Optimization of Deep Networks

https://github.com/snu-adsl/bbea

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B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

https://github.com/snu-mllab/bayesian-red-teaming

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Query-Efficient Black-Box Red Teaming via Bayesian Optimization

https://github.com/snu-mllab/discreteblockbayesattack

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Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization

https://github.com/snu-mllab/parsimonious-blackbox-attack

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Black-box Adversarial Attacks with Bayesian Optimization

https://github.com/softsys4ai/FlexiBO

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FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks

https://github.com/sooperset/boss

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Bayesian Optimization Meets Self-Distillation

https://github.com/souryadey/deep-n-cheap

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Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning

https://github.com/ssethz/mcbo

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Model-based Causal Bayesian Optimization

https://github.com/subhodipbiswas/BayesianEvolution

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Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

https://github.com/sygogo/alignment_beamlines_rl

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Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

https://github.com/symbench/anvil

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Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool

https://github.com/taicaichen/pg-lbo

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PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance

https://github.com/tanmay-ty/tntrules

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Explainable Bayesian Optimization

https://github.com/tennisonliu/llambo

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Large Language Models to Enhance Bayesian Optimization

https://github.com/theo-brown/bayesopt_with_invariances

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Sample-efficient Bayesian Optimisation Using Known Invariances

https://github.com/theo-brown/invariantkernels

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Sample-efficient Bayesian Optimisation Using Known Invariances

https://github.com/thiago-vasconcelos/no-past-bo

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No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization

https://github.com/thomasfirmin/hpo_snn

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Parallel Hyperparameter Optimization Of Spiking Neural Network

https://github.com/tiannuo-yang/vdtuner

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VDTuner: Automated Performance Tuning for Vector Data Management Systems

https://github.com/tkusmierczyk/bayesian_hyperparameters_matching

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Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching

https://github.com/tom-pelsmaeker/deep-generative-lm

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Effective Estimation of Deep Generative Language Models

https://github.com/toscanosaul/bayesian_quadrature_optimization

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Bayesian Optimization with Expensive Integrands

https://github.com/trsav/hitl-bo

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Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems

https://github.com/trustai/deepsava

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Sparse Adversarial Video Attacks with Spatial Transformations

https://github.com/tsingqaq/trieste

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$\{\text{PF}\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization

https://github.com/tsudalab/CRYSIM

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CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines

https://github.com/tuhh-ics/2024-code-l4dc-safe-multi-task-bayesian-optimization

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Towards Safe Multi-Task Bayesian Optimization

https://github.com/tuhh-ics/2025-code-an-analysis-of-safety-guarantees-in-multi-task-bayesian-optimization

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An Analysis of Safety Guarantees in Multi-Task Bayesian Optimization

https://github.com/tung-nd/tnp-pytorch

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Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

https://github.com/tyliu22/online_pacgp

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Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making

https://github.com/uber-research/TuRBO

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Scalable Global Optimization via Local Bayesian Optimization

https://github.com/uiuc-iml/autompc

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Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning

https://github.com/umdatasciencelab/consensus_bayesian_opt

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Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design

https://github.com/umdatasciencelab/llm-in-the-loop-bo

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LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization

https://github.com/umkiyoung/dibo

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Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization

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