Suppose I just invented merge sort, but due to my limited ability was only able to prove that the running time is $O(n^2)$. However, I suspect that the running time is actually better (in reality it's $O(n \log n)$).
Technically in my paper I should say “runtime $=O(n^2)$”, but it’s somewhat misleading because people think I mean it's a tight bound (i.e. people generally think big O actually means big theta). Therefore, I am considering saying “runtime $\leq O(n^2)$”, even though it's technically redundant. Obviously I will explain in the text what I mean, but I'm saying it many times for different equations and I don't want to keep repeating myself.
I am aware of the definitions of big O, Omega, Theta, but which is clearer in a CS paper? The paper is related to machine learning.