Systems and methods for training machine learning models using emulated datasets

Abstract

Systems and methods for training machine learning models using emulated datasets are provided. In some instances, it may be undesirable for certain portions of an initial training dataset to be used to train a machine learning model. In other instances, a model may be trained using an initial training dataset and it may be desired to cause the machine learning model to "forget" certain aspects of the training. To address both of these scenarios, a generative machine learning model may be used to generate an emulated training dataset that is based on the initial training dataset. The emulated training dataset is then used to train the machine learning model that is ultimately used to perform downstream tasks. This allows the machine learning model to still be trained to perform the task, without the risk of the machine learning model being exposed to the portions of the training data that are undesired to be used for training.

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