March 13, 2024, 4:42 a.m. | Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

cs.LG updates on

arXiv:2403.07442v1 Announce Type: new
Abstract: We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels. In this setting, neither the covariate shift nor the label shift assumptions apply. Our approach to adaptation employs proximal causal learning, a technique for estimating causal effects in settings where proxies of unobserved confounders are available. We demonstrate that proxy variables …

abstract apply arxiv assumptions change cs.lg distribution domain domain adaptation labels shift study type

Senior Data Engineer

@ Displate | Warsaw

Decision Scientist

@ Tesco Bengaluru | Bengaluru, India

Senior Technical Marketing Engineer (AI/ML-powered Cloud Security)

@ Palo Alto Networks | Santa Clara, CA, United States

Associate Director, Technology & Data Lead - Remote

@ Novartis | East Hanover

Product Manager, Generative AI

@ Adobe | San Jose

Associate Director – Data Architect Corporate Functions

@ Novartis | Prague