  Modern applications of machine learning (ML) deal with increasingly
heterogeneous datasets comprised of data collected from overlapping latent
subpopulations. As a result, traditional models trained over large datasets may
fail to recognize highly predictive localized effects in favour of weakly
predictive global patterns. This is a problem because localized effects are
critical to developing individualized policies and treatment plans in
applications ranging from precision medicine to advertising. To address this
challenge, we propose to estimate sample-specific models that tailor inference
and prediction at the individual level. In contrast to classical ML models that
estimate a single, complex model (or only a few complex models), our approach
produces a model personalized to each sample. These sample-specific models can
be studied to understand subgroup dynamics that go beyond coarse-grained class
labels. Crucially, our approach does not assume that relationships between
samples (e.g. a similarity network) are known a priori. Instead, we use
unmodeled covariates to learn a latent distance metric over the samples. We
apply this approach to financial, biomedical, and electoral data as well as
simulated data and show that sample-specific models provide fine-grained
interpretations of complicated phenomena without sacrificing predictive
accuracy compared to state-of-the-art models such as deep neural networks.
