Controllable Pareto front learning (CPFL) approximates the Pareto solution set and then locates a Pareto optimal solution with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice, so instead of training on the entire decision space, we only trained on the constraint region. Controllable Pareto front learning with Split Feasibility Constraints (SFC) is a way to find the best Pareto solutions to a split multi-objective optimization problem that meets certain constraints. In the previous study, CPFL used a Hypernetwork model comprising multi-layer perceptron (Hyper-MLP) blocks. With the substantial advancement of transformer architecture in deep learning, transformers can outperform other architectures in various tasks. Therefore, we have developed a hyper-transformer (Hyper-Trans) model for CPFL with SFC. We use the theory of universal approximation for the sequence-to-sequence function to show that the Hyper-Trans model makes MED errors smaller in computational experiments than the Hyper-MLP model.
A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
Faculty of Applied Mathematics and Informatics, Hanoi University of Science and Technology（应用数学与信息技术学院，河内科学技术大学）；College of Engineering and Computer Science, VinUniversity（工程与计算机科学学院，Vin大学）
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；University of Oxford（牛津大学）；School of Computer Science, Beijing Institute of Technology（北京理工大学计算机学院）；Nanyang Technological University（南洋理工大学）
Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment
Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所FIT）；University of Cologne（科隆大学）；University of Stuttgart（斯图加特大学）；University of Southampton（南安普顿大学）；Soochow University（苏州大学）；University Hospital of Cologne（科隆大学医院）
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Mila（米拉研究所）；École Polytechnique de Montréal（蒙特利尔综合理工学院）；Université de Montréal（蒙特利尔大学）；ServiceNow（ServiceNow公司）；Recursion Pharmaceuticals（Recursion制药公司）
多目标优化（MOO）问题是机器学习中普遍存在的问题。这些问题有一组最优解，称为帕累托前沿，其中前沿上的每个点代表可能冲突的目标之间的不同权衡。最近的MOO方法可以在损失空间中瞄准特定的期望射线，然而，大多数方法仍然面临两个严重的限制：（i）必须为前面的每个点训练单独的模型;以及（ii）在优化过程之前必须知道确切的权衡。在这里，我们解决了学习整个帕累托前沿的问题，并能够在训练后在前沿上选择所需的操作点。我们称之为帕累托前沿学习（Pareto Front Learning，PFL）。我们描述了一种使用超网络实现的PFL方法，我们将其称为帕累托超网络（PHN）。PHN使用单个超网络同时学习整个帕累托前沿，该超网络接收所需的偏好向量作为输入，并返回帕累托最优模型，其损失向量在所需的射线中。与训练多个模型相比，统一模型具有运行时效率，并且可以推广到训练期间未使用的新操作点。我们评估我们的方法在广泛的问题，从多任务回归和分类公平。PHN学习整个帕累托前沿的时间与学习前沿上的单个点的时间大致相同，并且同时达到更好的解集。此外，我们还证明了PHN可以扩展以生成像ResNet 18这样的大型模型。PFL为新的应用程序打开了大门，在新的应用程序中，模型是根据仅在运行时间可用的首选项来选择的。
Improving Pareto Front Learning via Multi-Sample Hypernetworks
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；School of Applied Mathematics and Informatics, Hanoi University of Science and Technology（河内科技大学应用数学与信息学院）
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems at Southern University of Science and Technology（计算机科学与工程系和可信自主系统研究 institute at 南方科技大学）
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
Vrije Universiteit Brussel（布鲁塞尔自由大学）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；University of Galway（戈尔韦大学）；City of Amsterdam（阿姆斯特丹市）；Utrecht University（乌得勒支大学）
Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences（萨尔茨堡应用科学大学智能与安全工业自动化约瑟夫·雷塞尔中心）；Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg（萨尔茨堡巴黎洛德龙大学人工智能与人类交互系）
Multi-Objective Bayesian Optimization via Adaptive \varepsilon-Constraints Decomposition
Department of Computer Science, Aalto University, Espoo, Finland（阿尔托大学计算机科学系，芬兰 Espoo）；ELLIS Institute Finland（芬兰 ELLIS 机构）；Department of Computer Science, University of Manchester, Manchester, United Kingdom（曼彻斯特大学计算机科学系，英国 Manchester）
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
University of Oxford（牛津大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Microsoft Research Asia（微软亚洲研究院）；Peking University（北京大学）；University of Alberta（阿尔伯塔大学）
