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.

通过训练技术和应用设置，本文将 PtO 方法扩展到具有不可微分的有序加权平均目标的优化问题，展示了如何在不确定性下有效地将 OWA 函数优化与参数预测相结合，以实现公平和稳健的优化。