Microsoft Technology Licensing, LLC

Yu Gong

Qi Guo

Daniel Sairom Krishnan Hewlett

Dan Liu

Wenxiang Chen

Xiaoyi Zhang

Lester Gilbert Cottle, Iii

Xuebin Yan

Haitong Tian

Siyao Sun

Pei-Lun Liao

In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Two-stage training with non-randomized and randomized data

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Santa Clara, CA, United States of America

Yu Gong