arXiv AI June 15, 2026 By Zikai Xie, Linjiang Chen

From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces

arXiv:2507. 13263v4 Announce Type: replace-cross Abstract: Bayesian Optimization (BO) is a powerful tool for black-box optimization, but its application to high-dimensional permutation spaces is severely limited by the challenge of defining scalable representations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jun 29

Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians

arXiv:2503. 13051v3 Announce Type: replace Abstract: Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts.

By Kai Uwe Barthel, Florian Barthel, Peter Eisert

arXiv Machine Learning

Jun 2

LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems

arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.

By Yihong Huang, Chen Chu, Fei Chen, Yu Lin, Ruiduan Li, Zhihao Li

rag efficiency benchmarks

arXiv AI

Aug 11

PRISM: A Predictive Protocol for Permutation Optimization via Landscape Diagnostics

arXiv:2608. 08344v1 Announce Type: cross Abstract: Permutation optimization arises whenever the components of a system are fixed but their ordering affects performance.

By Blessings Mambwe

benchmarks

arXiv Machine Learning

5d ago

Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization

arXiv:2608. 12687v1 Announce Type: new Abstract: Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation.

By Fin Amin, Sounak Dutta, Paul D. Franzon

rag

arXiv Machine Learning

Jun 30

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.

By Zhibin Duan, Yuhong Wang, Jiahong Fu, Zongsheng Yue, Bo Chen, Zongben Xu

fine-tuning benchmarks

arXiv AI

Jun 9

Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution

arXiv:2606. 08904v1 Announce Type: new Abstract: Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems.

By Shibing Mo, Jing Liu, Jianchu Xu, Ruilin Wu

llms benchmarks

Related stories

Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians

LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems

PRISM: A Predictive Protocol for Permutation Optimization via Landscape Diagnostics

Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution