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 which have given rise
to the target failure, and fixing the model through erasing information about them. This …

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 often 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 which have given rise
to the target failure, and fixing the model through erasing information about them. This …