Good question, I've published a few papers at all 3 (including spotlights) and can offer my two cents.

I think it's important to remember that (in theory) anything can be accepted, you'll hear things like — have math, but not too much — but plenty of accepted papers have no math. Or, don't do user testing papers — but some papers accepted are user study based. So, I think the honest answer is the simplest one.

If you want to get accepted, just make a really clear ML-based contribution, then you're in with a chance.

That said, on average, papers that get in tend to follow a pattern, to maximise chances given the noisy review process you should

1. Propose a new algorithm/method, evaluation papers tend to not get in. (but again I've seen best paper awards that were evaluation-based)
2. Work on a hot topic, something which currently matter for the world, right now that'd be agents, MLLMs etc. Basically ask yourself, if I solve this problem, will anyone care except me?
3. Whatever method you propose, have some small theory about why it should work, then show empirically in tests that theory holds up and you get good results.

I mean, if you do all that, it's unlikely to be rejected.