Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables
Aleksandr Karakulev Dave Zachariah Prashant Singh
We present an efficient parameter-free approach for statistical learning from
corrupted training sets. We identify corrupted and non-corrupted samples using
latent Bernoulli variables, and therefore formulate the robust learning problem
as maximization of the likelihood where latent variables are marginalized out.
The resulting optimization problem is solved via variational inference using an
efficient Expectation-Maximization based method. The proposed approach improves
over the state-of-the-art by automatically inferring the corruption level and
identifying outliers, while adding minimal computational overhead. We
demonstrate our robust learning method on a wide variety of machine learning
tasks including online learning and deep learning where it exhibits ability to
adapt to different levels of noise and attain high prediction accuracy.