In the present environment where privacy protection is increasingly emphasized, source-free unsupervised domain adaptation (SFUDA) has garnered more attention compared to standard unsupervised domain adaptation (UDA). It concentrates on transferring knowledge directly from well-trained source models to unlabeled target domains without requiring the involvement of source domain like UDA, greatly enhancing data protection capabilities. Many existing methods employ pseudo-labeling to guide this process, but due to domain shift, pseudo-labels often introduce significant noise. Although there are methods to filter out this noise and mitigate its impact, they may also result in the loss of crucial sample knowledge, leading to performance deterioration. In contrast, we propose a novel approach called Progressive Curriculum Learning with Teacher-Student Collaboration (PCTSC) method to mitigate the adverse influence of noisy labels in SFUDA. Inspired by curriculum learning, PCTSC assesses samples’ learning difficulty and trains models in an incremental manner from easy to hard, thereby enhancing the capability of model to against noise. Furthermore, PCTSC employs a two-stage learning approach: initially, a teacher model directs the student model, and later, the student model transitions to independent learning. We assess the effectiveness of PCTSC by conducting extensive experiments across three benchmark datasets, demonstrating its robustness against pseudo-label noise in SFUDA setting.

Predictive analytics has been widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines (MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good.