Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize (PtO) paradigm in machine learning aims to maximize downstream decision quality by training the parametric inference model end-to-end with the subsequent constrained optimization. This requires backpropagation through the optimization problem using approximation techniques specific to the problem's form, especially for nondifferentiable linear and mixed-integer programs. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives, known for their ability to ensure properties of fairness and robustness in decision models. Through a collection of training techniques and proposed application settings, it shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous System（可信自主系统研究院）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation（广东省类脑智能计算重点实验室）；School of Data Science, Lingnan University（岭南大学数据科学学院）
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
Thales Air Systems（泰雷兹空中系统公司）；Thales Research & Technology（泰雷兹研究与技术公司）；INRIA Saclay Ile-de-France（法国国家信息与自动化研究所萨克雷-法兰西岛分部）；Université Paris Sud（巴黎第十一大学）
Training-Free Diffusion-Driven Modeling of Pareto Set Evolution for Dynamic Multiobjective Optimization
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究所）；Department of Artificial Intelligence, School of Informatics, Xiamen University（厦门大学信息学院人工智能系）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education（教育部多媒体可信感知与高效计算重点实验室）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室）；Department of Artificial Intelligence, Sichuan University（四川大学人工智能系）
Florida International University（佛罗里达国际大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Mississippi（密西西比大学）；National Institutes of Health（美国国立卫生研究院）
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Lawrence Berkeley National Laboratory（伯克利劳伦斯国家实验室）；Université Paris-Saclay, CNRS/IN2P3, IJCLab（巴黎萨克雷大学，CNRS/IN2P3，IJCLab）；ChaLearn；University of Washington（华盛顿大学）；University of California, Berkeley（加州大学伯克利分校）；University of Toronto（多伦多大学）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；University of California, San Diego（圣地亚哥大学）
