Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition

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Transfer Learning for Activity Recognition: A Survey (2013, methodology): “[23] develop an importance weighted least-squares probabilistic classification approach to handle transfer learning when P(Xs) = P(Xt ) (i.”
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, background): “Several recent papers [25], [34], [35] aim to estimate the optimal weights by solving least-square based formulations.”
A Review of Domain Adaptation without Target Labels (2019, background): “through training on web data [45], and recognizing movements across sensors [46] or persons [47], [48].”
Optimizing for the Future in Non-Stationary MDPs (2020, methodology): “To capture this idea formally, we build upon the insights of Hachiya et al. (2012) and Mahmood et al. (2014), who draw an equivalence between weighted least-squares (WLS) estimation and the weighted importance sampling (WIS) (Precup, 2000) estimator.”
An introduction to domain adaptation and transfer learning (2018, background): “Computer vision systems that recognize activities have to adapt across different surroundings as well as different groups of people [192, 90, 71].”