large-scale-data

This paper investigates distributed sketching for OLS regression, where sketches are built from partitioned subsets rather than the whole dataset, reducing computational cost. The authors characterize the exact excess loss of the averaged estimator and show it matches that of whole-data sketching when subset covariance divergence is small.

This paper explores the use of variational autoencoders to learn latent representations of large-scale X-ray scattering data, enabling efficient data compression and analysis.