Robust fairness under covariate shift
WebWe investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We … WebWe investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels.
Robust fairness under covariate shift
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WebRobust causal inference under covariate shift via worst-case subpopulation treatment effects Sookyo Jeong [email protected] Stanford University ... In Conference on Fairness, Accountability and Transparency, pages 77–91, 2024. Carlos Carvalho, Avi Feller, Jared Murray, Spencer Woody, and David Yeager. Assessing treat- WebOur formulation seeks a robust and fair predictor under the covariate shift assumption by playing a minimax game augmented by a fairness penalty between a minimizing predictor against a worst-case approximator of the target distribution that matches the feature statistics of the source.
Web1 day ago · To illustrate the effect of these contaminations, Figures 1 and 2 display the functionals boxplots of the classical and robust estimators of the area under the curve AUC z and the Youden index Y I z, respectively.Functional boxplots, introduced by Sun and Genton (2011), are a useful tool to visualize a collection of curves.The area in purple represents … Web1 day ago · The attention is placed on those situations where the presence of covariates related to the diagnostic marker may increase the discriminating power of the ROC curve. Recent robust procedures given in the framework of the induced methodology are extended to the situation where functional covariates are also present.
WebMay 18, 2024 · We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We... http://proceedings.mlr.press/v125/jeong20a/jeong20a.pdf
WebWe investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We …
WebWe investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We … lawn mower parts belfastWebWe seek fair decisions under these assumptions on target data with unknown labels.We propose an approach that obtains the predictor that is robust to the worst-case in terms … lawn mower parts buffalo nyWebThe first paper dealing with the intersection between covariate shift and fairness from a robust learning point of view: Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. “Robust Fairness Under Covariate Shift”, AAAI2024. lawn mower parts beaverton oregonWebMay 18, 2024 · We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label … lawn mower parts bowling greenWebOct 11, 2024 · We seek fair decisions under these assumptions on target data with unknown labels.We propose an approach that obtains the predictor that is robust to the worst-case … lawn mower parts bellingham waWebIn practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution ... kampf reality stars 2022WebOct 11, 2024 · A framework for bounding violations of statistical fairness subject to distribution shift is developed, formulating a generic upper bound for transferred fairness … lawn mower parts brunswick ga