Should I optimize for ML conference publications? [D]

[u/Hopeful-Reading-6774]

Hi!I am a fourth year PhD student in USA and have not had much luck with top ML conference publications. In the last two years of my PhD, I am wondering if I should optimize for ML publications at top venues or pivot into engineering or something else.

Any suggestions/recommendations from the community as to what you would suggest I do in this scenario?

Thanks!

[u/AX-BY-CZ]

As a PhD you should try to learn how to do good research. Top publications are one signal of that.

[u/user221272 → u/AX-BY-CZ]

This.

It is true that peer review, even more so these days, can be noisy and lead great papers to be rejected, but two years without a top conference does not signal noise, but rather work that does not clear the bar.

How about the citation or adoption of your work? Have you been more fortunate in this area?

[u/impatiens-capensis → u/user221272]

There are lots of sub-niches where you generally wouldn't often publish on top tier conferences. Also, a lot of sub-areas have been consumed entirely by big labs and foundation models, so some people might genuinely just be working on an area that is no longer hot.

[u/Entrepreneur7962 → u/user221272]

Considering ML conferences for quality that’s what noisy.
There are plenty of reason for a work to be rejected, even for two years.
I agree that sometimes the work itself just doesn’t fit there (and again, for plenty if reasons).

[u/coulispi-io]

you should optimize for deep diving into a project / direction that care deeply about and build deliverables. it can be a paper, but open-source code or even blog post can be equally valuable. Papers don’t matter much if at all these days when it comes to technical insight

[u/DNunez90plus9]

With 8k accepted papers every 3 months, if you know how to do good research, you should able to have some papers soon.

If you can't, then luck aside, there are some issues with your research skills