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Consider making a prediction over new test data without any opportunity tolearn from a training set of labelled data - instead given access to a set ofexpert models and their predictions alongside some limited information aboutthe dataset used to train them. In scenarios from finance to the medicalsciences, and even consumer practice, stakeholders have developed models onprivate data they either cannot, or do not want to, share. Given the value andlegislation surrounding personal information, it is not surprising that onlythe models, and not the data, will be released - the pertinent questionbecoming: how best to use these models? Previous work has focused on globalmodel selection or ensembling, with the result of a single final model acrossthe feature space. Machine learning models perform notoriously poorly on dataoutside their training domain however, and so we argue that when ensemblingmodels the weightings for individual instances must reflect their respectivedomains - in other words models that are more likely to have seen informationon that instance should have more attention paid to them. We introduce a methodfor such an instance-wise ensembling of models, including a novelrepresentation learning step for handling sparse high-dimensional domains.Finally, we demonstrate the need and generalisability of our method onclassical machine learning tasks as well as highlighting a real world use casein the pharmacological setting of vancomycin precision dosing.

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning