Before revision:  Also, for k-means clustering , we give a faster alternative for retaining worst-case guarantees. 2) We show empirically that learned sketches are reliable in improving approximation accuracy, with comparison against "non-learned" sketching baselines. 3) We introduce a greedy algorithm for optimizing the location of the nonzero entries of a sparse sketch and prove guarantees for certain distributions on the LRA task. Previous work only looked at optimizing the values rather than the locations. Also, we show empirically that it further improves learned sketch performance . \\%DIF > and 20\\%, respectively.
After revision:  Also, for k-means clustering . \\%DIF > and 20\\%, respectively.