Fairness-Regularized Online Optimization with Switching Costs
具有切换成本的公平性正则化在线优化
机构 * School of Information(信息学院) ; Rochester Institute of Technology(罗切斯特技术学院) ; Electrical and Computer Engineering(电气与计算机工程) ; University of California, Riverside(加州大学河滨分校) ; Computing & Mathematical Sciences(计算与数学科学) ; California Institute of Technology(加州理工学院)
AI总结 本文提出FairOBD算法,通过引入辅助变量将长期公平性成本分解为在线成本,有效减少公平性正则化成本并促进公平结果。
Comments Accepted by NeurIPS 2025