Adobe, Inc.

Pratiksha Agarwal

Balaji Krishnamurthy

Nikaash Puri

Piyush Gupta

Sukriti Verma

Dheeraj Bansal

Vishal Wani

Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs&#x2014;since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions&#x2014;identified based on output of the proxy black-box model&#x2014;using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

Rule determination for black-box machine-learning models

Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.

Personalized dynamic content via content tagging and transfer learning

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Delhi, India

Pratiksha Agarwal