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Found 2 papers, 1 papers with code
GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts
The predictive variance of the model takes into account both the variance due to data density and photometric noise.
Instrumentation and Methods for Astrophysics I.2.6
A Sparse Gaussian Process Framework for Photometric Redshift Estimation
The proposed framework reaches a mean absolute $\Delta z = 0. 0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$ on the simulated data, and $\Delta z = 0. 0178(1+z_\textrm{s})$ over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature.
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