Integrated Conditional Estimation-Optimization
提出集成条件估计-优化框架，在估计随机参数条件分布时考虑下游优化问题结构，实现渐近一致性和有限样本保证，并通过可微近似提升梯度算法性能。
Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem. We directly model the relationship between the conditional distribution of the random parameter and the contextual features and then estimate the probabilistic model with an objective that aligns with the downstream optimization problem. We show that our ICEO approach is asymptotically consistent under moderate regularity conditions and further provide finite performance guarantees. Computationally, performing estimation with the ICEO approach is a nonconvex and often nondifferentiable optimization problem. We propose a general methodology for approximating the potentially nondifferentiable mapping from estimated conditional distribution to the optimal decision by a differentiable function, which greatly improves the performance of gradient-based algorithms. Numerical experiments demonstrate the empirical success of our approach in different situations, including with limited data samples and model mismatches.