Viet Anh Nguyen (VinAI) – Robust Bayesian Recourse

Algorithmic recourse aims to recommend an informative feedback to overturn an unfavourable 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 the robust recourse, 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.

Papers related to the talk: https://openreview.net/forum?id=BqIM6SIoqgq

Viet Anh Nguyen is a research scientist at VinAI, Vietnam. Previously, he was a postdoctoral scholar at the Department of Management Science and Engineering, Stanford University. He holds a B.Eng and a M.Eng from the National University of Singapore, a French engineering diploma (Diplome d’Ingenieur) from Ecole Centrale Paris, and a Ph.D. from Ecole Polytechnique Federale de Lausanne. He is interested in very large-scale decision making under uncertainty, statistical optimization and machine learning with applications in autonomous systems, operations management, and data/policy analytics.

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