跳出循环的隐式监督，多保真贝叶斯优化迎来新范式，降本增效直击采样痛点。
arXiv:2608.04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expens…
瞄准局部约束下的黑箱优化难题，提出新方法并在ICML 2026发表，值得关注。
arXiv:2603.07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant c…
用贝叶斯锚定潜在信任区域优化高维不确定下的结构设计分类问题，突破传统贝叶斯优化局限
arXiv:2604.25241v2 Announce Type: replace Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design…
贝叶斯优化化身化学实验向导，用更少试错锁定最佳反应条件，科研效率直接起飞。
arXiv:2502.18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple su…
用概率框架给LLM当“侦探”，从模型迷宫中自动发现隐藏结构，开箱即用的新思路。
arXiv:2602.18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a pro…
融合信赖域与贝叶斯优化的新型局部-全局框架，为复杂优化问题提供高效解法。
arXiv:2603.02970v2 Announce Type: replace Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and g…
提出极简主义的Thompson采样方法MINTS，用最少的假设实现高效决策，贝叶斯优化领域的新思路。
arXiv:2606.01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but …
用语言模型引导贝叶斯优化，高效搜索LoRA超参数，ICML 2026论文带来微调新思路。
arXiv:2602.11171v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-e…
大模型驱动自动特征工程，协作贝叶斯超参数优化提效，KDD 2026前沿方法。
arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottlenec…
用大语言模型突破传统黑盒优化瓶颈，语义引导高效探索昂贵实验空间。
arXiv:2510.25404v3 Announce Type: replace-cross Abstract: Optimizing an experimental system can be extremely challenging when each experiment is expen…
无需知晓下游任务的具体数据，仅凭反馈即可动态优化训练数据混合——DUET算法将影响函数与贝叶斯优化结合，理论保证收敛到最优混合比例，为LLM数据选择开辟了全新范式。
arXiv:2502.00270v3 Announce Type: replace-cross Abstract: The performance of an LLM depends heavily on the relevance of its training data to the downs…