3 accepted papers
Heterogeneous data from multiple populations, sub-groups, or sources can be represented as a "mixture model" with a single latent class influencing all of the observed covariates. Heterogeneity can be resolved at different levels by grouping populations according to different notions of similarity.…
Statistical prediction models are often trained on data that is drawn from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focu…
Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this often rely on estimating the ratio of training and target probability density functions. These techniques require parameter…