2024 Robust fairness under covariate shift

Robust fairness under covariate shift

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August undefined, 2024

WebOct 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 in terms of target performance while satisfying target fairness requirements and matching statistical properties of the source data. Webto be robust with respect to adversarial examples. Focal loss [39] encourages the learning algorithm to focus on more difﬁcult examples by up-weighting examples proportionate to their losses. Domain adaptation work requires a model to be robust and generalizable across different domains, under either covariate shift [53, 48] or label shift [40].

Fairness Violations and Mitigation under Covariate Shift

WebFeb 10, 2024 · We consider popular fairness criteria that depend on the following quantities: the group-specific prediction rates ( \Pb(R=1∣A) ); positive predictive values (PPVs: \Pb(Y =1∣A,R=1)) and negative predictive values (NPVs: \Pb(Y =0∣A,R=0) ); and the error rates, meaning the false positive rates (FPRs: \Pb(R=1∣A,Y =0) ), and false negative rates … WebJul 4, 2024 · Dai and Brown studies fairness under label distributional shift, while we focus on covariate shift. 2.2 Model Robustness and Smoothness Model generalization ability … people ready terre haute indiana

arXiv:2010.05166v1 [cs.LG] 11 Oct 2024

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 · 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. toggle output scrolling

Robust Fairness Under Covariate Shift - CaltechAUTHORS

Fairness Transferability Subject to Bounded Distribution Shift

Webfair_covariate_shift. This is the code for our paper Robust Fairness Under Covariate Shift published in AAAI 2024. Abstract. Making predictions that are fair with regard to … WebJun 19, 2024 · Covariate shift has been shown to sharply degrade both predictive accuracy and the calibration of uncertainty estimates for deep learning models. This is worrying, because covariate shift is prevalent in a wide range of real world deployment settings. However, in this paper, we note that frequently there exists the potential to access small … toggle ortho mode hotkey autocadWebOct 11, 2024 · A framework for bounding violations of statistical fairness subject to distribution shift is developed, formulating a generic upper bound for transferred fairness … toggle overlay lock

"Webunder data shift, the covariate shift assumption holds if we use only the separating set of features for prediction. Our model builds on robust classiﬁcation method of (Liu and … " - Robust fairness under covariate shift

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 seek fair decisions under these assumptions on target data with unknown labels. 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 …

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WebApr 12, 2024 · Towards Robust Tampered Text Detection in Document Image: New dataset and New Solution ... FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding ... Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations Hagay Michaeli · Tomer Michaeli · Daniel Soudry 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 …

WebAssociation for the Advancement of Artificial Intelligence 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...

Webing tasks. We introduce a generalization of robust covariate shift classiﬁcation that allows the inﬂuence of covariate shift to be limited to different feature-based views of the relationship between input variables and example labels. We demonstrate the beneﬁts of this approach on classiﬁcation under covariate shift tasks. 1 Introduction WebOct 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 …

Webfair_covariate_shift. This is the code for our paper Robust Fairness Under Covariate Shift published in AAAI 2024.. Abstract. Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms.

WebThe 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. toggle output macbook proWebOct 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 in terms of target... toggle output button double-clickWebFairness Violations and Mitigation under Covariate Shift FAccT ’21, March 3–10, 2024, Virtual Event, Canada the two distributions may be different (e.g. data from two hospitals with different care practices). Bold letters are used for vectors, uppercase for random variables, and lowercase for instantiations. 3.1 Fair classifier toggle overlay lock not savingWebIn 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 ... toggle overlay meaningWebRobust Fairness under Covariate Shift Ashkan Rezaei1, Anqui Liu2, Omid Memarrast1, Brian Ziebart1 1 Departmentof Computer Science, University of Illinois at Chicago 2 California Institute of Technology [email protected], [email protected], [email protected], [email protected] Abstract Making predictions that are fair with regard to protected toggle overlay meaning discordWebWe 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 … peopleready thorntonWebWe 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 … toggle overlay discord not working