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Unsupervised Risk Estimation with only Structural Assumptions

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Given a model θ and unlabeled samples from a distribution p , we show how to estimate the labeled risk of θ while only making structural (i.e., conditional independence) assumptions about p. This lets us estimate a model’s test error on distributions very different than its training distribution, thus performing unsupervised domain adaptation even without assuming the true predictor remains constant (covariate shift). Furthermore, we can perform discriminative semi-supervised learning, even under model mis-specification. Our technical tool is the method of moments, which allows us to exploit conditional independencies without relying on a specific parametric model. Finally, we introduce a new theoretical framework for grappling with the non-identifiability of the class identities fundamental to unsupervised learning.

This talk is part of the Machine Learning @ CUED series.

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