基于多元插值的可行追索权规划
Feasible Recourse Plan via Diverse Interpolation
摘要：
解释算法决策和推荐可操作的反馈对于机器学习应用越来越重要。最近，已经投入了大量的努力来寻找一组不同的资源，以覆盖广泛的用户偏好。然而，现有的工作往往忽略了资源应该接近的数据流形的要求，因此，构造的资源可能是令人难以置信的，用户不满意。为了解决这些问题，我们提出了一种新的方法，明确地将不同的可操作资源集指向数据流形。我们首先在有利的类别中找到一组多样化的原型，以平衡多样性和邻近性之间的权衡。我们展示了两种具体的方法来找到这些原型：无论是通过寻找最大的后验估计的一个决定性的点过程或通过解决二次二元规划。为了保证可操作性约束，我们构造了一个可操作性图，其中节点表示训练样本，边表示两个实例之间的可行动作。然后，我们找到一条通往每个原型的可行路径，这条路径展示了计划中每个资源的可行操作。实验结果表明，我们的方法产生了一组接近数据流形的资源，同时提供了比现有方法更好的成本多样性权衡。
英文摘要：
Explaining algorithmic decisions and recommending actionable feedback is increasingly important for machine learning applications. Recently, significant efforts have been invested in finding a diverse set of recourses to cover the wide spectrum of users' preferences. However, existing works often neglect the requirement that the recourses should be close to the data manifold; hence, the constructed recourses might be implausible and unsatisfying to users. To address these issues, we propose a novel approach that explicitly directs the diverse set of actionable recourses towards the data manifold. We first find a diverse set of prototypes in the favorable class that balances the trade-off between diversity and proximity. We demonstrate two specific methods to find these prototypes: either by finding the maximum a posteriori estimate of a determinantal point process or by solving a quadratic binary program. To ensure the actionability constraints, we construct an actionability graph in which the nodes represent the training samples and the edges indicate the feasible action between two instances. We then find a feasible path to each prototype, and this path demonstrates the feasible actions for each recourse in the plan. The experimental results show that our method produces a set of recourses that are close to the data manifold while delivering a better cost-diversity trade-off than existing approaches.
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