Abstract: A multiple platform, multiple creative testing system and method implement and utilize devices and processes for managing and implementing testing of multiple creatives across multiple independent online platforms. The system and method enable a provider of creatives to perform testing of multiple creatives, such as A/B testing, across multiple online platforms with an A test targeting a control group of targeted individuals and an independent B test concurrently targeting a disjoint test group of targeted individuals across multiple online platforms. The system and method accomplish testing through concurrently using two independent targeted tests utilizing separate creatives to disjoint groups of targeted individuals across multiple online platforms.

Abstract: A knowledge distillation system and method trains neural networks utilizing a non-conventional relay buffer and augmented data tuples. In at least one embodiment, the knowledge distillation system and method pretrain a teacher model that implements a contextual bandit algorithm. A lightweight student model determines online contextual bandit data tuples as to context x, arm/action a, and reward/payoff r. The data tuples are stored in a replay buffer. The teacher model randomly samples data tuples from the replay buffer and augments the sampled data tuples. Augmented data tuples are stored in the replay buffer. The student model batch processes augmented data tuples to update parameters of contextual bandit data tuples.