Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While BO budgets are typically given in iterations, this implicitly measures convergence in terms of iteration count and assumes each evaluation has identical cost. In practice, evaluation costs may vary in different regions of the search space. For example, the cost of neural network training increases quadratically with layer size, which is a typical hyperparameter. Cost-aware BO measures convergence with alternative cost metrics such as time, energy, or money, for which vanilla BO methods are unsuited. We introduce Cost Apportioned BO (CArBO), which attempts to minimize an objective function in as little cost as possible. CArBO combines a cost-effective initial design with a cost-cooled optimization phase which depreciates a learned cost model as iterations proceed. On a set of 20 black-box function optimization problems we show that, given the same cost budget, CArBO finds significantly better hyperparameter configurations than competing methods.
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche（国家研究委员会信息科学与技术研究所）；Artificial Intelligence Center University of Oviedo（奥维耶多大学人工智能中心）
Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)
ISCT, Bures-sur-Yvette, France（法国ISCT）；Institute of Digital Twin (IDT), EIT, Ningbo, China（中国宁波EIT数字孪生研究所）；Dept. of Statistics, Central South University, Changcha, China（中国长沙中央南大学统计系）
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data
Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institute of Reliability Engineering, Beihang University（北京航空航天大学可靠性工程研究所）；Science & Technology on Reliability & Environmental Engineering Laboratory（可靠性与环境工程技术重点实验室）；School of Reliability and Systems Engineering, Beihang University（北京航空航天大学可靠性与系统工程学院）
Symmetry-Aware Bayesian Optimization via Max Kernels
School of Computer and Communication Sciences, École polytechnique fédérale de Lausanne (EPFL)（计算机与通信科学学院，瑞士联邦理工学院（EPFL））；Institute of Mathematics, École polytechnique fédérale de Lausanne (EPFL)（数学学院，瑞士联邦理工学院（EPFL））
针对贝叶斯优化中利用对称性但 max kernel 非半正定的问题，提出其 PSD 投影核，在不增加计算复杂度的情况下显著降低合成与真实基准上的遗憾。
Bayesian Entropy Neural Networks for Physics-Aware Prediction
School for Engineering of Matter, Transport and Energy（物质、运输与能源工程学院）；Arizona State University（亚利桑那州立大学）；School of Computing and Augmented Intelligence（计算与增强智能学院）
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Department of Stochastic Simulation and Safety Research for Hydrosystems（随机模拟与安全研究部门）；Cluster of Excellence SimTech（卓越中心SimTech）；University of Stuttgart（斯图加特大学）；Department of Statistics（统计学系）；TU Dortmund University（多特蒙德技术大学）
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Fudan University（复旦大学）；Shenzhen Loop Area Institute（深圳河套学院）；Shanghai Jiao Tong University（上海交通大学）；Central South University（中南大学）；Huazhong Agricultural University（华中农业大学）
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
Zhejiang University（浙江大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；Aalborg University（奥尔堡大学）；Cyprus University of Technology（塞浦路斯理工大学）
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
St. Petersburg State University（圣彼得堡国立大学）；University of Cambridge（剑桥大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Bosch Center for Artificial Intelligence（博世人工智能中心）；Imperial College London（帝国理工学院）；St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences（俄罗斯科学院斯捷克洛夫数学研究所圣彼得堡分所）
Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection
Department of Electrical and Computer Engineering, Johns Hopkins University（约翰霍普金斯大学电气与计算机工程系）；Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室）
Cost-Aware Bayesian Optimization for Prototyping Interactive Devices
Aalto University Helsinki Finland；University of Cambridge Cambridge United Kingdom；Aalto University \& ELLIS Institute Helsinki Finland；Aalto University；University of Cambridge；Aalto University \& ELLIS Institute
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
Mitsubishi Electric Research Laboratories（三菱电机研究院）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室）
