  We create classical (non-quantum) dynamic data structures supporting queries
for recommender systems and least-squares regression that are comparable to
their quantum analogues. De-quantizing such algorithms has received a flurry of
attention in recent years; we obtain sharper bounds for these problems. More
significantly, we achieve these improvements by arguing that the previous
quantum-inspired algorithms for these problems are doing leverage or
ridge-leverage score sampling in disguise; these are powerful and standard
techniques in randomized numerical linear algebra. With this recognition, we
are able to employ the large body of work in numerical linear algebra to obtain
algorithms for these problems that are simpler or faster (or both) than
existing approaches. Our experiments demonstrate that the proposed data
structures also work well on real-world datasets.
