论文标题
强大的贝叶斯追索权
Robust Bayesian Recourse
论文作者
论文摘要
算法追索权旨在推荐提供丰富的反馈，以推翻不利的机器学习决策。我们在本文中介绍了贝叶斯追索权，这是一种模型不足的追索权，可最大程度地减少后验概率比值比。此外，我们介绍了其最小稳健的对应物，目的是抵制机器学习模型参数的未来变化。强大的对应物明确考虑了使用最佳运输（Wasserstein）距离规定的高斯混合物中数据的可能扰动。我们表明，所得最差的目标函数可以分解为求解一系列二维优化子问题，因此，最小值追索问题发现问题可与梯度下降算法相提并论。与现有的生成稳健回流的方法相反，可靠的贝叶斯追索不需要线性近似步骤。数值实验证明了我们所提出的稳健贝叶斯追索的有效性，面临模型转移。我们的代码可在https://github.com/vinairesearch/robust-bayesian-recourse上找到。
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnostic recourse that minimizes the posterior probability odds ratio. Further, we present its min-max robust counterpart with the goal of hedging against future changes in the machine learning model parameters. The robust counterpart explicitly takes into account possible perturbations of the data in a Gaussian mixture ambiguity set prescribed using the optimal transport (Wasserstein) distance. We show that the resulting worst-case objective function can be decomposed into solving a series of two-dimensional optimization subproblems, and the min-max recourse finding problem is thus amenable to a gradient descent algorithm. Contrary to existing methods for generating robust recourses, the robust Bayesian recourse does not require a linear approximation step. The numerical experiment demonstrates the effectiveness of our proposed robust Bayesian recourse facing model shifts. Our code is available at https://github.com/VinAIResearch/robust-bayesian-recourse.
