Two new research papers introduce novel approaches to enhance the efficiency of Bayesian optimization (BO) in high-dimensional and large-budget scenarios. The first paper, GRAPE, refines local gradients and uses progress-aware exploitation to achieve significant speedups in adversarial attacks and LLM prompt optimization. The second paper, GSSBO, proposes a gradient-based sample selection strategy to reduce the computational cost of Gaussian process fitting in BO, demonstrating comparable optimization performance with reduced computational expense. AI
IMPACT These advancements in Bayesian optimization could accelerate research and development in areas requiring efficient optimization of complex functions, such as hyperparameter tuning and adversarial attacks.
RANK_REASON Two academic papers published on arXiv introducing new methods for Bayesian optimization.
- alphaXiv
- arXiv
- Bayesian optimization
- CatalyzeX Code Finder for Papers
- CORE Recommender
- DagsHub
- Gaussian process
- Gotit.pub
- GRAPE
- GSSBO
- Hugging Face
- machine learning
- Qiyu Wei
- Richard Cornelius SUWANDI
- ScienceCast
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