Abstract: A system and method for continual learning in a provider network. The method is configured to implement or interface with a system which implements a semi-automated or fully automated architecture of continual machine learning, the semi-automated or fully automated architecture implementing user-configurable model retraining or hyperparameter tuning, which is enabled by a provider network. This functions to adapt a model over time to new information in the training data while also providing a user-friendly, flexible, and customizable continual learning process.

Abstract: In factorization of binary matrices or tensors, training algorithms usually scale linearly with the number of training examples. For very unbalanced learning problems, the number of non-zero training examples can be much smaller than the number of zeros in the full dataset. For some problems where the squared norm can be efficiently computed, the training time complexity can be reduced. A method herein receives a binary tensor defined by matrices comprising elements in a database. A processing device determines an upper bound for non-quadratic losses associated with factorization of the binary tensor. The upper bound is based on a variation parameter. The processing device performs factorization of the binary tensor by alternately minimizing the upper bound with respect to the variation parameter and minimizing the upper bound with respect to the elements of the matrices using a gradient descent method.