Jpmorgan Chase Bank, N.a.

Zhonghao Shi

Richard Chen

Marco Pistoia

Shaohan Hu

William R Moriarty

Wei-Cheng Huang

Hsiang Hsu

A method may include: receiving an original dataset and a sensitive attribute; initializing a distilled dataset from the original dataset; initializing a classification model; sampling original dataset data and an original dataset label from the original dataset, and distilled dataset data and a distilled dataset label from the distilled dataset; providing the sampled original dataset data from the original dataset to the classification model, resulting in an original dataset prediction probability; providing the sampled distilled dataset data from the distilled dataset to the classification model, resulting in a distilled dataset prediction probability; calculating a distilled dataset cross-entropy loss for the distilled dataset prediction probability and the distilled dataset label; calculating distilled dataset gradients for the distilled dataset cross-entropy loss; calculating a distance between the gradients as a matching loss; and updating the distilled dataset with the matching loss.

Systems and methods for fairness-aware data distillation for attribute classification

A method for removing uninterested attributes from multi-modality data may include: receiving, by a multi-modality attribute removal computer program executed by an electronic device, multi-modality data comprising a plurality of modalities from a data source, wherein data in each modality are related; receiving, by the multi-modality attribute removal computer program, an uninterested attribute in the multi-modality data to remove; training, by the multi-modality attribute removal computer program, a modality-focused encoder for each modality of the multi-modality data to remove the uninterested attribute using a removal loss and a retention loss for the respective modality; receiving, by the multi-modality attribute removal computer program, a multi-modality data set for processing; and processing, by the multi-modality attribute removal computer program, the multi-modality data set using the modality-focused encoders, wherein the processing results in a processed multi-modality data set with the uninterested attribute removed and one or more interested attribute retained.

Systems and methods for removal of attributes from multi-modality and multi-attribute data

Growing community of inventors

Los Angeles, CA, United States of America

Zhonghao Shi