Robust Wasserstein Profile Inference

在不确定性下，任何数据驱动的决策问题的一个关键部分都涉及能够以高度的置信度保证在实际部署时估计的决策规则的理想性能。这正是统计推断所提供的保证的关键作用。该项目的目标是研究一种新型推理方法，该方法从一开始就精确构建具有增强的样本外属性的数据驱动决策规则。这是通过引入博弈论公式来实现的，在该公式中，决策者在向数据添加扰动（在合理的大小内，但任意方向）时，针对最佳利用决策的潜在弱点的对手进行优化。一个统计框架的目的是估计最佳的数据扰动量，以获得强大的，但实际的，决策规则。该框架自然会导致最佳（在某种意义上）的基本决策参数的置信区域。该项目的产出在不确定性下的应用决策的各个领域都有影响，特别是机器学习，人工智能，运营管理和运输是特别感兴趣的应用。研究生将致力于大规模优化运输的计算方法。在这个项目中要研究的新的推理方法统一和扩展了一大类估计（如广义Lasso和正则化逻辑回归等），这些估计已成功地应用于实践。这些都包含在一个分布式鲁棒优化（DRO）框架。DRO公式是一类游戏，其中统计员选择一个参数或一个动作来最小化某些预期损失，对手选择在一定大小（称为分布不确定性大小）内对统计员的经验测量（最大化预期损失）进行扰动。在这个建议的背景下，这种扰动是衡量的最佳运输成本（或Wasserstein距离）。Wasserstein距离和DRO公式的使用证明了这种推理方法的名称Robust Wasserstein Profile Inference的合理性。该建议研究的最佳选择的分布不确定性大小和相关的最佳置信区域诱导的DRO制定。具体应用，例如，形状约束估计和随机优化问题在工程中将研究的PI。拟议的研究提供了一个丰富的最优运输理论，统计推断和凸优化之间的相互作用。最后，PI将努力从代表性不足的群体中招募高素质的人员。PI还将通过开放获取网站传播该提案的科学成果，以及标准工具（会议和期刊出版物）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A crucial part of any data-driven decision-making problem under uncertainty involves being able to guarantee, with a high degree of confidence, a desirable performance of estimated decision rules when actually deployed in practice. This, precisely, is the key role of the guarantees given by statistical inference. The goal of this project is to investigate a novel inference methodology that precisely builds from inception data-driven decision-making rules with enhanced out-of-sample properties. This is achieved by introducing a game-theoretic formulation, in which the decision maker optimizes against an adversary that optimally exploits potential weaknesses of a decision when adding perturbations to the data (within reasonable size, yet arbitrary directions). A statistical framework is designed to estimate an optimal amount of data perturbations to obtain robust, yet practical, decision rules. The framework naturally leads to optimal (in certain sense) confidence regions for the underlying decision making parameters. The output of this project has implications in various areas of applied decision making under uncertainty, in particular, machine learning, artificial intelligence, operations management, and transportation are applications of special interest. The graduate student will work on computational methods for large scale optimal transport. The novel inference methodology to be investigated in this project unifies and extends a large class of estimators (such as generalized Lasso and regularized logistic regression among many others), which have been successfully applied in practice. These are encompassed within a distributionally robust optimization (DRO) framework. A DRO formulation is a class of games in which the statistician chooses a parameter or an action to minimize certain expected loss and an adversary chooses a perturbation of the empirical measure against the statistician (maximizing the expected loss) within a certain size (called the distributional uncertainty size). In the context of this proposal, this perturbation is measured in terms of optimal transport costs (or Wasserstein distances). The use of the Wasserstein distance and the DRO formulation justifies the name Robust Wasserstein Profile Inference of this inference methodology. The proposal studies the optimal selection of the distributional uncertainty size and associated optimal confidence regions induced by the DRO formulation. Specific applications, for example, to shape constrained estimation and stochastic optimization problem in engineering will be studied by the PI. The proposed research provides a rich interplay between the theory of optimal transport, statistical inference and convex optimization. Finally, the PI will attempt to recruit high-quality personnel from under-represented groups. The PI will also disseminate the scientific output of this proposal via open access sites, in addition to the standard vehicles (conferences and journal publications).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.