Microsoft Technology Licensing, LLC

Pei-Lun Liao

Qi Guo

Daniel Sairom Krishnan Hewlett

Dan Liu

Wenxiang Chen

Xiaoyi Zhang

Lester Gilbert Cottle, Iii

Xuebin Yan

Yu Gong

Haitong Tian

Siyao Sun

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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Sunnyvale, CA, United States of America

Pei-Lun Liao