English(EN) BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference

新方法通过潜在空间推断和流形搜索解决黑盒优化问题

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-07 04:00

研究人员开发了一种新的约束黑盒优化方法，将问题重新表述为生成模型潜在空间内的后验推断。该方法利用流模型和扩散模型来有效地搜索满足复杂约束的最优解。另外，还引入了一个名为 BOOOM 的框架，用于斜交流形上的损失函数无关的黑盒优化，该框架利用新颖的基于旋转的参数化进行无导数搜索。 AI

影响引入了新颖的优化技术，可以提高机器学习算法在复杂、受限环境中的效率和适用性。

排序理由该集群包含两篇学术论文，详细介绍了机器学习和统计推断的新颖优化技术。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Kiyoung Om, Kyuil Sim, Taeyoung Yun, Hyeongyu Kang, Jinkyoo Park · 2026-05-08 04:00

Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization

arXiv:2507.00480v2 Announce Type: replace Abstract: Optimizing high-dimensional black-box functions under black-box constraints is a pervasive task in a wide range of scientific and engineering problems. These problems are typically harder than unconstrained problems due to hard-…
arXiv cs.LG TIER_1 English(EN) · Beomchang Kim, Subhrajyoty Roy, Priyam Das · 2026-05-07 04:00

BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference

arXiv:2605.04087v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold $\mathrm{St}(p,d)$, the set of $p \times d$ column-orthonormal matrices, is fundamental in statistics, machine learning, and scientific computing, yet remains challenging in the presence of n…