Adaptive Robust Learning using Latent Bernoulli Variables

arXiv:2312.00585v2 Announce Type: replace-cross
Abstract: We present an adaptive approach for robust learning from corrupted training sets. We identify corrupted and non-corrupted samples with latent Bernoulli variables and thus formulate the learning problem as maximization of the likelihood where latent variables are marginalized. The resulting 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, while adding minimal computational overhead. We demonstrate our robust learning …

abstract arxiv cs.lg identify inference likelihood problem replace robust samples stat.ml training type variables via