Recent research papers explore advancements in Bayesian Optimization (BO) techniques for complex problems. One study introduces "Out-Of-The-Loop" MF-BO, which incorporates historical high-fidelity data to improve optimization when direct access to the highest fidelity is too costly. Another paper presents a bilevel BO approach that exploits separability in problems with black-box and white-box variables, outperforming standard methods on benchmarks. Additionally, a framework called "Tempered Posteriors" is proposed to enhance BO robustness by adjusting Gaussian process surrogates, showing improved performance under localized sampling. Finally, a dynamic prior framework for BO in hyperparameter optimization allows for continuous user influence, demonstrating consistent outperformance against competitors. AI
IMPACT These advancements in Bayesian Optimization offer more robust and efficient methods for tackling complex optimization problems in scientific discovery and machine learning development.
RANK_REASON The cluster consists of four academic papers published on arXiv, detailing novel methods and theoretical analyses within Bayesian Optimization.
- arXiv
- Bayesian optimization
- CatalyzeX
- DagsHub
- Daugavpils
- Gotit.pub
- Hugging Face
- hyperparameter optimization
- Lukas Fehring
- ScienceCast
- alphaXiv
- Gaussian process
- Jiguang Li
- reproducing kernel Hilbert space
- CatalyzeX Code Finder for Papers
- CORE Recommender
- emotional intelligence
- IArxiv Recommender
- Influence Flower
- Maria Filomena Botelho
- Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures
- Raspberry Pi
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