2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020
DOI: 10.1109/cvpr42600.2020.00922
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NestedVAE: Isolating Common Factors via Weak Supervision

Abstract: Fair and unbiased machine learning is an important and active field of research, as decision processes are increasingly driven by models that learn from data. Unfortunately, any biases present in the data may be learned by the model, thereby inappropriately transferring that bias into the decision making process. We identify the connection between the task of bias reduction and that of isolating factors common between domains whilst encouraging domain specific invariance. To isolate the common factors we combi… Show more

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Cited by 11 publications
(5 citation statements)
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“…The prevalence of reports of systemic bias arising from automated decision processes is increasing, and an awareness for sources of bias is critical in undertaking fair and equitable machine learning [97,169,224,256]. Just because causal discovery methods define themselves as 'causal', does not mean there are not significant difficulties associated with taking the leap from data to reality.…”
Section: The Causal Leapmentioning
confidence: 99%
“…The prevalence of reports of systemic bias arising from automated decision processes is increasing, and an awareness for sources of bias is critical in undertaking fair and equitable machine learning [97,169,224,256]. Just because causal discovery methods define themselves as 'causal', does not mean there are not significant difficulties associated with taking the leap from data to reality.…”
Section: The Causal Leapmentioning
confidence: 99%
“…For example, VCCA [Wang et al, 2016] formulates a model that samples different views of a common object from distributions conditioned on a shared latent variable. NestedVAE [Vowels et al, 2020] learns the common factors using staged information bottlenecks by training a low-level VAE given the latent space derived from a high-level VAE. In our model, given an input sequence, we treat multiple time frames as the different "views" of a common underlying factor which is the global factor.…”
Section: Related Workmentioning
confidence: 99%
“…The prevalence of reports of systemic bias arising from automated decision processes is increasing, and an awareness for sources of bias is critical in undertaking fair and equitable machine learning [99,168,219,248]. Just because causal discovery methods define themselves as 'causal', does not mean there are not significant problems with taking the leap from data to reality.…”
Section: The Causal Leapmentioning
confidence: 99%