(I wrote this because this was something I inferred over time, and I think it's helpful to new reviewers to explain acceptance criteria more explicitly)

That is, a paper should provide either a unique result or a unique idea (ideally both), and on top of that should have no correctness issues.

Full post is here: www.austintripp.ca/blog/2025-06...

Happy to hear comments/feedback! I know my approach is just 1 of many!

If none of this makes any sense to you but you think multi-objective optimization is relevant, check out my full post below (where I explain MOO in more detail too). Bonus: also has an interactive visualization (kudos to Claude 3.7)

www.austintripp.ca/blog/2025-05...

2. Unfortunately, maximizing the Chebyshev objective may produce points which are *not* Pareto optimal (so some filtering might be required)

...

(also, with ICML reviewing starting, this post will probably be the first in a series of posts about peer reviewing, stay tuned! 👀)

I wrote a blog post explaining this in more detail: www.austintripp.ca/blog/2025-02...

If you think I'm wrong, I'd genuinely like to hear why. Please comment in 🧵

Second note: there are a lot of more standard topics too, eg AI for science stuff, I'm just not posting that here.

Also funny:

- Position: ML researchers should try to ensure their code is not a heaping pile of dogsh*t

- Position: ML researchers should learn basic math (I'm talking to you, people who don't add error bars to their plots!!)

- Position: focusing on meaningless benchmarks is stupid

Other abstracts:

- Position: what if we started holding ML papers to actual standards?

- Position: reviewers should actually read the papers they are reviewing

- Position: reviewers should *at least try* to judge whether paper's claims are true before accepting them

(Note: titles are summarized/anonymized since I don't think I'm allowed to share)

Slightly longer version of this post is on my blog: www.austintripp.ca/blog/2025-01...