Inspired by behavioral science, we propose Behavior Learning (BL), a novel general-purpose machine learning framework that learns interpretable and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization. BL parameterizes a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, the latter modeling hierarchical optimization structures. Its smooth and monotone variant (IBL) guarantees identifiability. Theoretically, we establish the universal approximation property of BL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high-dimensional data. Code: https://github.com/MoonYLiang/Behavior-Learning ; install via pip install blnetwork.
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning
Facultad Regional Villa María, UTN（UTN玛丽亚城地区学院）；CIT Villa María - CONICET-UNVM（玛丽亚城跨学科研究中心 - CONICET-UNVM）；Instituto de Desarrollo y Diseño CONICET-UTN（CONICET-UTN发展与设计研究所）
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Institute of AI for Health, Computational Health Center, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所（计算健康中心））；Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University Munich（慕尼黑工业大学）；U.S. FDA/CDRH（美国食品药品监督管理局医疗器械和辐射健康中心）；Applied Geology, GeoZentrum Nordbayern, Friedrich-Alexander-Universität（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学北巴伐利亚地理中心应用地质学系）
Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems
Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）；Department of Electrical and Electronic Engineering, University of Sheffield（谢菲尔德大学电子与电气工程系）
Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems
IRIT, UMR 5505 CNRS, Université Toulouse Capitole, Université de Toulouse（图卢兹计算机科学研究所，法国国家科学研究中心联合研究单位5505，图卢兹第一大学，图卢兹大学）；ONERA, DTIS, Université de Toulouse（法国国家航空航天研究院，信息处理与系统部，图卢兹大学）；Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse（法国国立民航大学-法国国立高等航空航天学院-法国国家航空航天研究院联合会，图卢兹大学）；German Aerospace Center (DLR), Institute of System Architectures in Aeronautics（德国航空航天中心，航空系统架构研究所）
Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
IIT Kanpur（印度理工学院坎浦尔分校）；University of Maryland, College Park（马里兰大学学院公园分校）；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas, Austin（德克萨斯大学奥斯汀分校）；Oracle（Oracle公司）；University of Central Florida（中央佛罗里达大学）；University of Bath（巴斯大学）
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London（电气与电子工程系，工程学院，伦敦帝国理工学院）；School of Mathematics and Statistics, Beijing Jiaotong University（数学与统计学学院，北京交通大学）
Dalian University of Technology（大连理工大学）；University of Victoria（维多利亚大学）；Southern University of Science and Technology（南方科技大学）；National Center for Applied Mathematics Shenzhen（深圳国家应用数学中心）；Peng Cheng Laboratory（鹏城实验室）；The Hong Kong Polytechnic University（香港理工大学）
A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market
Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach
University of Electronic Science and Technology of China（电子科技大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局信息通信研究院）
