Microsoft Technology Licensing, LLC

Xuebin Yan

Qi Guo

Fei Chen

Joel Daniel Young

Daniel Sairom Krishnan Hewlett

Dan Liu

Wenxiang Chen

Xiaoyi Zhang

Songxiang Gu

Lester Gilbert Cottle, Iii

Chang-Ming Tsai

Haitong Tian

Siyao Sun

Pei-Lun Liao

Yu Gong

Andris Birkmanis

The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Unified parameter and feature access in machine learning models

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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Xuebin Yan