Techniques for improving machine-learning accuracy and convergence

Abstract

Systems and methods are described herein for reducing the computational burden related to performing one or more experiments. The set of item assets (e.g., images, text, features, descriptions, etc.) may be reduced in an intelligent manner to enable the set to include more disparate assets. The system may obtain vectors that describe each asset. A similarity score (or other indication/representation of similarity) may be presented for each pair of assets and displayed at a user interface. Using the similarity scores (or similarity representations) as a guide, the user may reduce the set of assets. The reduced set of assets may then be utilized to perform one or more experiments in order to identify an optimal selections from the assets. In some embodiments, the one or more experiments may utilize an explore/exploit algorithm (e.g., a multi-armed bandit algorithm) to identify an optimal selection of item assets.