FloodDAN: Unsupervised Flood Forecasting based on Adversarial Domain Adaptation

Zhou, Rui-Zhi; Pan, Yanling

doi:10.1109/bdai56143.2022.9862723

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The parameters in the objective are learned through RMSProp, which is an adaptive learning rate method that divides the learning rate by an exponentially decaying average of squared gradients. We choose RMSProp as the optimizer because it is a popular and effective method to determine the learning rate abortively which is widely used for training adversarial neural networks (Dou et al, 2019 ; Li et al, 2022 ; Zhou and Pan, 2022 ). Adam is another widely adopted optimizer that extends RMSProp with momentum terms, however, the momentum terms may make Adam unstable (Mao et al, 2017 ; Luo et al, 2018 ; Clavijo et al, 2021 ).…”

Section: F Air Da: Fair Classification With Domain...mentioning

confidence: 99%

Fair classification via domain adaptation: A dual adversarial learning approach

Liang

Chen²,

Tian³

et al. 2023

Front. Big Data

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Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to developing effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to pre-process the data, regularize the model learning or post-process the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from a similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.

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