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

Repairing Neural Networks by Leaving the Right Past Behind