System and Method for Ensemble Expert Diversification and Control Thereof

Machine readable and non-transitory medium having information stored thereon for machine learning, wherein the information, when read by the machine, causes the machine to perform:

• sending a training sample to an expert for training a model representative of the expert;
• receiving a prediction generated by the expert in accordance with the training sample and based on one or more parameters associated with the model;
• analyzing a metric with respect to the prediction characterizing the prediction;
• sending, when the metric satisfies a first criterion, a ground truth label associated with the training sample to the expert to facilitate the training.

A system for machine learning, comprising:

• a training data distribution unit configured for sending a training sample to an expert for training a model representative of the expert;
• and
• a ground truth allocation unit configured for
• receiving a prediction generated by the expert in accordance with the training sample and based on one or more parameters associated with the model,
• analyzing a metric with respect to the prediction characterizing the prediction, and
• sending, when the metric satisfies a first criterion, a ground truth label associated with the training sample to the expert to facilitate the training, wherein
• the one or more parameters of the model are updated by the expert during the training based on the prediction and the ground truth label.