Microsoft Technology Licensing, LLC

Excalibur IP, LLC

Miroslav Dudik

Daniel Gray Goldstein

John C Langford

Jake M Hofman

Alekh Agarwal

Leon Bottou

Siddhartha Sen

Lihong Li

Olivier Chapelle

Aleksandrs Slivkins

Yi Su

Maria Dimakopoulou

Sarah L Bird

Akshay Krishnamurthy

Various techniques for providing perspective annotation to numerical representations are disclosed herein. For example, a method includes detecting a numerical representation in an original content and retrieving one or more perspectives from a database based on the detected numerical representation. The one or more perspectives individually include a restatement of information contained in the numerical representation. The method can also include annotating the original content with the retrieved one or more perspectives to form an annotated content.

Perspective annotation for numerical representations

Off-policy evaluation of a new 'target' policy is performed using historical data gathered based on a previous 'logging' policy to estimate the performance of the target policy. An estimator may be used, wherein either a quality-based estimator or a quality-agnostic estimator is used to weight the difference between an observed reward in the historical data and an estimated reward generated by the target policy. A quality-agnostic estimator may be used to evaluate an importance weight according to a threshold. In such examples, when the importance weight exceeds the threshold, the quality-agnostic estimator clips the importance weight at the threshold, thereby providing an fixed upper bound irrespective of the quality of the reward predictor. In other examples, a quality-based estimator is used, in which an upper bound incorporates the quality of the reward predictor in order to modify an importance weight used by the estimator.

System for effective use of data for personalization

The claimed subject matter includes techniques for providing an application testing service. An example method includes receiving context information from a client system, the context information comprising parameters that describe details of a user interaction with an application under test (AUT). The method also includes receiving a set of potential actions from the client system. The method also includes identifying a selected action from the set of potential actions and sending the selected action to the client system, wherein the AUT is customized based on the selected action. The method also includes receiving reward data from the client system based on a user's interaction with the AUT. The method also includes storing the context information, the selected action, and the reward data to a log of application test data.

Application testing

Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.

Method and system for distributed machine learning

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Brooklyn, NY, United States of America

Miroslav Dudik