The robust Bayesian recourse aims to perturb directly the empirical conditional distributions of X|ˆY = y, which then reshapes the decision boundary in the covariate space in an adversarial manner.
Jun 22, 2022 · Abstract:Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision.
Robust Bayesian Recourse explicitly takes into account possible perturbations of the data in a Gaussian mixture ambiguity set prescribed using the optimal ...
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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 ...
The Bayesian recourse is introduced, a model-agnostic recourse that minimizes the posterior probability odds ratio and its min-max robust counterpart is ...
Jun 22, 2022 · Contrary to existing methods for generating robust recourses, the robust Bayesian recourse does not require a linear approximation step.
Robust Bayesian Recourse: a robust model-agnostic algorithmic recourse method (UAI'22) - robust-bayesian-recourse/run_expt.py at main ...
This package estimates an ensemble of meta-analytic models (assuming either the presence or absence of effect, heterogeneity, and publication bias)
Jun 22, 2022 · The robust Bayesian recourse aims to perturb directly the empirical conditional distributions of X|ˆY = y, which then reshapes the decision.
SUMMARY. Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the ...