zlacker

>>RobinH+(OP)
This does feel a bit like under grad introduction to statistical analysis and surprising anyone felt the need to explain these things. But I also suspect most AI people out there now a days have limited math skills so maybe it’s helpful?

>>fnordp+Are
As an ML researcher who started in physics (this seems common among physics/math turned ML people. Which Evan is included), I cannot tell you how bad is it... One year at CVPR when diffusion models hit the scenes I was asking what people's covariance was (I had overestimated the model complexity), and the most common answer I got was "how do I calculate that?" People do not understand things like what "pdf" means. People at top schools! I've been told I'm "gatekeeping" for saying that you should learn math (I say "you don't need math to build good models, but you do to understand why they're wrong"). Not that you need to, but should. (I guess this explains why Mission Impossible Language Models won best paper...)

I swear, the big reason models are black boxes are because we _want_ them to be. There's clear anti-sentiment mentality against people doing theory and the result of this shows. I remember not too long ago Yi Tay (under @agihippo but main is @YiTayML) said "fuck theorists". I guess it's not a surprise Deep Mind recently hired him after that "get good" stuff.

Also, I'd like to point out, the author uses "we" but the paper only has one author on it. So may I suggest adding their cat as a coauthor? [0]

[0] https://en.wikipedia.org/wiki/F._D._C._Willard

>>godels+5Ke
Personal sad story, but hopefully relevant: during my recent PhD I worked on a problem where I used a Dirichlet Process in my solution. That paper has been bouncing around for the past few years getting rejected from every venue I have submitted it to. My interpretation is that most reviewers (there are exceptions - too few to impact the final voting) don't understand any non-DL theory anymore and are not willing to read up for the sake of a fair review. This is based on their comments, where we have been told that our solution is complex (maybe? - but no one suggests an alternative), exposition is not clear (we have rewritten the paper a few times - we rewrite it based on comments from venue i to submit to venue i+1 - its a wild goose chase), and in one case, someone said the paper is derivative because it uses Blackwell-MacQueen sampling; their evidence? - they skimmed through a paper we had cited that also used the sampling algorithm. This is like saying a paper is derivative because it uses SGD.

I am on the review panel of some conferences too and it is not uncommon to be assigned a paper outside of my comfort zone. That doesn't mean I cut and bail. You set aside time, read up on the area, ask authors questions, and judge accordingly. Unfortunately this doesn't happen most of the time - people seem to be in a rush to finish their review no matter the quality. At this point, we just mechanically keep resubmitting the paper every once a while.

Sorry, end of rant :)

>>abhgh+Rrf
Is a preprint of your paper available?

I looked at your blog a bit and was able to find this, which may be it?

> Learning Interpretable Models Using Uncertainty Oracles

https://arxiv.org/abs/1906.06852

https://doi.org/10.48550/arXiv.1906.06852

>>aspenm+Cyf
Yes, that's the one: https://arxiv.org/pdf/1906.06852

>>abhgh+6zf
I copied the DOI for convenience but they’re the same paper.

I have no formal math background really so I can’t speak to your methods but I appreciate that you have shared your work freely.

Did you have any issues defending your thesis due to the issues you described above related to publishing?

Noticed a typo in your abstract:

“Maybe” should be “may be” in sentence below (italics):

> We show that this technique addresses the above challenges: (a) it arrests the reduction in accuracy that comes from shrinking a model (in some cases we observe ~ 100% improvement over baselines), and also, (b) that this maybe applied with no change across model families with different notions of size; results are shown for Decision Trees, Linear Probability models and Gradient Boosted Models.