  Prediction failures of machine learning models often arise from deficiencies
in training data, such as incorrect labels, outliers, and selection biases.
However, such data points that are responsible for a given failure mode are
generally not known a priori, let alone a mechanism for repairing the failure.
This work draws on the Bayesian view of continual learning, and develops a
generic framework for both, identifying training examples that have given rise
to the target failure, and fixing the model through erasing information about
them. This framework naturally allows leveraging recent advances in continual
learning to this new problem of model repairment, while subsuming the existing
works on influence functions and data deletion as specific instances.
Experimentally, the proposed approach outperforms the baselines for both
identification of detrimental training data and fixing model failures in a
generalisable manner.
