Training pipeline for training machine-learned user interface customization models

A Computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
• obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element;
• obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions:
• inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session;
• and
• obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request for a resource conditioned on rendering of the input element;
• determining the incremental probability of the request conditioned on rendering of the input element is below a threshold incremental probability;
• and
• updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements, wherein updating the second machine-learned model comprises:
• training the second machine-learned model using the respective weight to adjust future candidate proposals;
• and
• de-prioritizing, based on the incremental probability being below the threshold incremental probability, candidate proposals for participation in the real-time content selection process for populating the user interface with the input element.

One or more non-transitory computer readable media storing instructions that are executable by one or more processors to perform operations comprising:

• obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element;
• obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions:
• inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session;
• and
• obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request for a resource conditioned on rendering of the input element;
• determining the incremental probability of the request conditioned on rendering of the input element is below a threshold incremental probability;
• and
• updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements, wherein updating the second machine-learned model comprises:
• training the second machine-learned model using the respective weight to adjust future candidate proposals;
• and
• de-prioritizing, based on the incremental probability being below the threshold incremental probability, candidate proposals for participation in the real-time content selection process for populating the user interface with the input element.