Google Inc.

Alexander E Mayorov

Gang Wang

Wei Huang

Michael Daub

Rishav Anand

Joshua Patrick Gardner

Steven Guy Avery

Li He

The present disclosure describes techniques for training a model using cross-domain adaptation to classify content requests from client devices having unknown attributes. The system can obtain requests for content from client devices of a first domain, and requests for content from client devices of a second domain. The system can train a model by propagating request attributes of the first domain through the model to generate first internal data from an internal layer of the model and a first output vector an output layer of the model. The system can propagate request attributes of the second domain to generate second internal data, and determine a difference between the first internal data and the second internal data. The system can update the model based on the difference and the output vector, and classify a third client device of the second domain using the model.

Cookie space domain adaptation for device attribute prediction

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components to client devices in ways that protect user privacy and confidential data of content platforms and/or digital component providers are described. In one aspect, a method includes receiving, by a secure distribution system and from a client device of a user, a digital component request that includes, for each of multiple content platforms that distribute digital components to users, a corresponding user embedding comprising weights indicative of the relevance of multiple features to the user. The secure distribution system provides each user embedding as input to a respective isolated execution environment for the content platform corresponding to the user embedding, wherein the secure distribution system hosts each isolated execution environment. Digital component selection data generated based on the user embedding is received from each isolated execution environment.

Privacy preserving custom embeddings

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Kirkland, WA, United States of America

Alexander E Mayorov