W e only accept 1 out of 20 papers that do not cite any other paper from our own journal.

We initiate the study of a variant of the classic online speed scaling problem, in which machine learning predictions about the future can be integrated naturally.

The industry practice has been moving to embedding based retrieval (EBR).

(Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy.