Microsoft Technology Licensing, LLC

Hewlett Packard Enterprise Development LP

Okta, Inc.

Yu Liu

Eldad Perahia

Sachin Ganu

Qiang Zhou

Liang Zhang

Jun Shi

Andre Beaudin

Bo Long

Parvez Ahammad

Deven Mohan Patel

Shahnawaz Siraj

Huiji Gao

Charles Lukaszewski

Abhimanyu Lad

Ahmad Kholaif

Daniel Zeller

Lars Kristian Johansen

Venkat Ramanan Viswanathan

Shantanu Sardal

Michaeel M Kazi

Yu Gan

Gonzalo Jorge Aniano Porcile

Sanh Huynh

Ying Xiong

Jilei Yang

Fangfang Tan

Xiaowei Liu

Deven Patel

An authentication system facilitates efficient generation of authentication integrations with third-party identity providers for client systems. The authentication system provides one or more interfaces configured to receive requests to make authentication integrations available for a third-party identity provider. The requests to make authentication integrations available include integration information for the relevant identity provider. Based on the request to make authentication integrations available, the authentication system generates an identity provider profile for the identity provider that can be used to generate authentication integrations with the identity provider for one or more client systems. Once the identity provider profile is generated, the authentication system uses the identity provider profile to generate authentication integrations for one or more client systems that request authentication through the third-party identity provider. In doing so, the authentication system provides an efficient process for providing authentication integrations with identity providers requested by client systems.

Efficient generation of identity provider integrations

Techniques for detecting label shift and adjusting training data of predictive models in response are provided. In an embodiment, a first machine-learned model is used to generate a predicted label for each of multiple scoring instances. The first machine-learned model is trained using one or more machine learning techniques based on a plurality of training instances, each of which includes an observed label. In response to detecting a shift in observed labels, for each segment of one or more segments in multiple segments, a portion of training data that corresponds to the segment is identified. For each training instance in a subset of the portion of training data, the training instance is adjusted. The adjusted training instance is added to a final set of training data. The machine learning technique(s) are used to train a second machine-learned model based on the final set of training data.

Label shift detection and adjustment in predictive modeling

The disclosed embodiments provide a system for performing a natural language search. During operation, the system applies a first machine learning model to a natural language query to predict one or more search intentions associated with the natural language query. Next, the system applies a second machine learning model to the natural language query to produce one or more search parameters associated with a first intention in the search intention(s), wherein the search parameter(s) include a field and a value of the field. The system then performs a first search of a first vertical associated with the first intention using the search parameter(s). Finally, the system generates a ranking containing a first set of search results from the first search of the first vertical and outputs at least a portion of the ranking in a response to the natural language query.

Vertical processing of natural language searches

An example system can determine a power consumption of each of a first and a second powered device ports, determine an output voltage demand of each of the first and the second powered device ports based on the determined power consumptions, and based on the output voltage demand, adjust an output voltage to each of the first and the second powered device ports via the power controller.

Adjusting output voltage of powered device ports

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San Mateo, CA, United States of America

Yu Liu