Microsoft Technology Licensing, LLC

Justin Ormont

Marc Eliot Davis

Omar Rogelio Alonso

Vasileios Kandylas

Sarthak D Shah

Aidan C Crook

Yunling Wang

Systems and methods of automated machine learning for modeling a data set according to a modeling intent are presented. A modeling service receives a data set from a submitting party as well as a set of constraints. A pipeline generator generates a set of pipelines according to a modeling intent of a data set and in view of the set of constraints. A machine learned trained judge conducts an analysis of the pipelines to identify an optimal pipeline to train. Optimal results are generated according to the optimal pipeline and the optimal results are provided to the submitting party in response to receiving the data set and constraints.

Machine learning for automated model generation with constraints

One or more techniques and/or systems are provided for augmenting a social snippet with an augmentation tagline. For example, a search user may submit a search query through a search interface (e.g., 'algebra help' search query). An entity associated with the search query may be identified (e.g., a math professor). A set of taglines associated with the entity may be retrieved (e.g., descriptive information about the math professor extracted from social networks, documents, websites, etc.). A social snippet for the entity may be augmented with an augmentation tagline selected from the set of taglines or generated based upon information relating to the entity. The augmented social snippet may be displayed through the search interface, and the augmentation tagline may indicate a relevance of the entity to the search user (e.g., an indication that the math professor teaches algebra at a university attended by the search user).

Social snippet augmenting

Systems and methods for providing improved news results to a news query according to entity expansion are presented. In response to receiving a news query from a computer user, a news engine identifies one or more interest entities of the computer user. Expanded entity data corresponding to the one or more interest entities is obtained, the expanded entity data identifying related entities to the one or more interest entities. The expanded entity data also includes category data corresponding to the categories of the one or more interest entities. Expanded news queries are generated according to the interest entities, the related entities, and category entities corresponding to the categories. News results are obtained according to the expanded news queries and a news presentation is generated and returned to the computer user.

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Mountain View, CA, United States of America

Justin Ormont