Random projections are widely used to reduce data dimension in various analyses. Provable guarantees were developed first in the important result of Johnson and Lindenstrauss on Lipschitz maps, but more recently there has been a lot of follow-up work in the context of machine-learning.

Particularly attractive are *sparse random projections,* which share similar guarantees as the original proposal, but are much faster to compute. In my recent paper I improve upon previous results of Meena Jagadeesan from NIPS’19. The key idea is to consider **entropy of input data**, a more fine-grained approach than in prior works. In the mathematical analysis I show this gives superior statistical guarantees. Besides that, the novel analysis seems to be cleaner and simpler.

The paper has been already **reviewed and graded high** at STACS’21, yet the committee members somehow didn’t want this machine-learning inspired topic in their program:

The PC members agreed that this is a non-trivial and interesting contribution. However, we had a very tough competition, and in the end, the PC members felt that the results were maybe too specialised for STACS audience

I am resubmitting it to a venue which will better appreciate the combination of ML and theory. I am also going to revisit this note and give a friendly overview of the work 🙂