Linear least-squares regression with a “design” matrix A approximates a given matrix B via minimization of the spectral- or Frobenius-norm discrepancy ||AX − B|| over every conformingly sized matrix X. Also popular is low-rank approximation to B through the “interpolative decomposition,” which traditionally has no supervision from any auxiliary matrix A. The traditional interpolative decomposition selects certain columns of B and constructs numerically stable (multi)linear interpolation from those columns to all columns of B, thus approximating all of B via the chosen columns. Accounting for regression with an auxiliary matrix A leads to a “regression-aware interpolative decomposition,” which selects certain columns of B and constructs numerically stable (multi)linear interpolation from the corresponding least-squares solutions to the least-squares solutions X minimizing ||AX − B|| for all columns of B. The regression-aware decompositions reveal the structure inherent in B that is relevant to regression against A; they effectively enable supervision to inform classical dimensionality reduction, which classically has been restricted to strictly unsupervised learning.