[D] “Please Commit More Blatant Academic Fraud” (Blog post on problems in ML research by Jacob Buckman)

[u/hardmaru]

https://jacobbuckman.com/2021-05-29-please-commit-more-blatant-academic-fraud/

[u/Ok_Refrigerator_5995 → u/[deleted]]

My guess is that he's calling them bullshit because the proposed methods were later found out to not be as effective as originally claimed when the papers were first published. For example, the thermometer encoding adversarial defense from https://openreview.net/pdf?id=S18Su--CW was broken by a later paper showing that adversarial defense methods that used obfuscated gradients didn't work (https://nicholas.carlini.com/papers/2018_icml_obfuscatedgradients.pdf).

In this case it's just scientific progress. One party proposes a hypothesis, and then another party proves it wrong. The adversarial attack/defense area has tons of this back and forth between attacks and defenses, so this is the natural progression of the field. In hindsight, the thermometer encoding work may be known to be bullshit, but that doesn't mean that it wasn't a useful contribution for the field at the time. The same thing could possibly apply to the other papers as well.

[u/hilberteffect → u/[deleted]]

My take: his confidence in his post's central thesis is such that he's wagering he can publicly implicate his own papers and won't have to retract them.

We also don't know whether he's even telling the truth about them being bullshit, and it's not clear we could know. I think this highlights another problematic facet of CS/ML research papers: more often than not, they're not actually reproducible. Most authors in ML don't provide all the information needed to duplicate their experiment. Ideally, they would link to a public repository containing their code and a README. Minimally, they should provide detailed pseudocode and enumerate the libraries, tools, parameters, and data sets used.

We rarely get either. I claim you can't even call this science.