Microsoft Technology Licensing, LLC

Yi Su

Qiang Wu

Jianfeng Gao

Krysta Marie Svore

Elbio Renato Torres Abib

Chris J C Burges

Miroslav Dudik

Maria Dimakopoulou

Akshay Krishnamurthy

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

Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted.

Boosting algorithm for ranking model adaptation

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Yi Su