I would disagree that it's okay to treat college applications like games of deception. Yes the system is incredibly stupid BUT it has a large real impact on your life and that's what makes the difference. It might make your life better or more comfortable but that's what makes it a relevant moral problem. And if you get tempted by that, you'll probably be tempted by those high-paying zero-sum/exploitative careers post-graduation and then congrats -- you've sold out.
Besides, a country that lets a system which selects new elites based on vice rather than merit persist deserves the decay that comes with that.
Finally, I'm not sure if I'm extrapolating too much from my own experience, but I feel like if you're really competent you can do great regardless of elite uni acceptance. Most of the demand is from those seeking high-paying and/or comfortable zero-sum/exploitative careers, which you shouldn't want anyway.
Yeah I was originally envisioning this as an ML theory paper which is why it's math-heavy and doesn't have experiments. Tbh, as far as I understand, my paper is far more useful than most ML theory papers because it actually engages with empirical phenomena people care about and provides reasonable testable explanations.
Ha, I think some rando saying "hey I have plausible explanations for two mysterious regularities in ML via this theoretical framework but I could be wrong" is way more attention-worthy than another "I proved RH in 1 page!" or "I built ASI in my garage!"
Mmm, I know how to do "good" research. I just don't think it's a "good" use of my time. I honestly don't think adding citations and a lit review will help anybody nearly as much as working on other ideas.
PS: Just because someone doesn't flash their credentials, doesn't mean they don't have stellar credentials ;)
Oh yes I do know math lol. Yeah the summary above hits most of the main ideas if you're not too familiar with pure math.
Thanks interesting! I had not read this paper before.
Some initial thoughts:
I recently wrote an ML theory paper which proposes explanations for mysterious phenomena in contemporary machine learning like data scaling laws and double descent. Here's the link to the paper and the Twitter thread. I didn't get much attention and need an endorser to publish on ArXiv so I thought I'd post it here and get some feedback (and maybe an endorser!)
Essentially what the paper does is propose that all data in a statistical learning problem arises from a latent space via a generative map. From this we derive an upper bound on the true loss as depending on the training/empirical loss, the distance in latent space to the closest training sample... (read 519 more words →)
Perhaps! I'm not familiar with extended norms. But when you say "let's put the uniform norm on " warning bells start going off in my head 😅
Okay I took the nerd bait and signed up for LW to say:
For your example to work you need to restrict the domain of your functions to some compact e.g. because the uniform norm requires the functions to be bounded.
Also note this example works because you're not using the "usual" topology on which also includes the uniform norm of the derivative and makes the space complete. It is much more difficult if the domain is complete!
I don't think you understood me. I totally hate the system and wish it were different.
I was responding to the OP stating that it's not immoral to deceive on college applications!