A General Approach to Domain Adaptation with Applications in Astronomy
Published in PASP, 2018
Recommended citation: Vilalta R., Dhar Gupta K., Boumber D., Meskhi M. M., “A General Approach to Domain Adaptation with Applications in Astronomy”, Publications of the Astronomical Society of the Pacific (PASP), 2018, IOP Science Press.
Excerpt
In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation.
Recommended citation:
Vilalta R., Dhar Gupta K., Boumber D., Meskhi M. M., “A General Approach to Domain Adaptation with
Applications in Astronomy”, Publications of the Astronomical Society of the Pacific (PASP), 2018,
IOP Science Press.