Unsupervised Latent Debiasing of Time-Series Models

Traditional training regimens for time-series models have been shown to encode the biases from their training corpora into the models themselves. We aim to train unbiased time-series models using existing biased datasets. However, most debiasing techniques rely on explicit labels that encapsulate the bias, such as pairs of words along some worrying axis of bias such as race or gender for language models. We propose an unsupervised latent debiasing training regimen based on [2] that simultaneously learns the latent distribution of the dataset and a separate language task; datapoints are selected for training batches by sampling weights inverse to their commonality as determined by their placement in the latent space. We adapt [2] to time-series datasets and show algorithmic improvements to bias identification and bias reduction for models trained on toy and real datasets.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Phillips, Jacob

Advisor dc:contributor.advisor

• Rus, Daniela L.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143324

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143324