Lauren Anderson (CCA) and I discussed the state of our project to put spectroscopic parameters onto photometrically discovered stars using colors and magnitudes from APASS, parallaxes from Gaia TGAS, and spectroscopic parameters from the RAVE-on Catalog. We want to take the nearby neighbors in color-magnitude space and deconvolve their noisy spectroscopic parameters to make a less noisy estimate for (what you might call) the test objects. We have been using extreme deconvolution (Bovy et al.) for this, deconvolving the labels for the nearest neighbors (weighted by a likelihood). That is, find neighbors first, deconvolve second. After hours staring at the white board, we decided that maybe we should just deconvolve all the inputs up front, and do inference under the prior created by that deconvolution. Question: Is this computationally feasible?