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The article has a link[1] to a discussion between the blog author and the paper author that I find revealing. Perhaps as a reminder, the issue is that the pape

The article has a link[1] to a discussion between the blog author and the paper author that I find revealing. Perhaps as a reminder, the issue is that the paper’s implementation of their 2-nearest neighbor secretly uses an oracle to break ties, which obviously inflates the accuracy compared to a real-world kNN classifier that has to choose heuristically. To be fair, this could be a weird implementation accident and not malice. But I think it does invalidate the results. But rather than admit error, the author defends this choice, and does so using (in my opinion) dubious statistical arguments. Which leads me to believe that — at least at this point — they know they made a mistake and just won’t admit it. They claim that instead of a real-world accuracy, they wanted to find the “max” accuracy that their classifier was statistically capable of. That is, the accuracy you get if the stars happen to align and you get the luckiest possible result. Well, not only is this creative new metric not described in the paper, it’s also not applied to the other algorithms. For example, I think a neural network is capable of achieving a “max” accuracy of 100%, if all the initial weights happen to perfectly encode both the training and test sets. But of course they just use standard training to give the numbers for those algorithms. [1] https://github.com/bazingagin/npc_gzip/issues/3 https://github.com/bazingagin/npc_gzip/issues/3

catgary 3y ago
Of course they won’t admit they made a mistake, they’re watching a career-making paper become a retraction (especially with the training data contamination issues).
deleted 3y ago
[deleted]
ks2048 3y ago
Well put. Yes, I mention a similar case towards the end of that exchange: Consider a random-guess classifier. That has a max accuracy of 100%. Clearly, not a useful measure on its own.
lalaland1125 3y ago
In academia, it's better to cling to obviously false justifications to dismiss criticism and keep a paper accepted than to admit fault and potentially be forced to retract. Publish or perish
- bonzini 3y ago
  Retracting is extremely rare in computer science, which is why instead many conferences have started "stamping" papers that have artifacts which provide reproducible results.
hinkley 3y ago
A couple of AI hype cycles ago, everyone was abuzz about genetic algorithms. I recall a cautionary tale that was related about someone using FPGAs to do genetic algorithms. After a while they noticed several disturbing things. One, that the winners had fewer gates than theory thought was necessary to solve the problem. Two, some days the winners didn't work, and three, sometimes the winners didn't work on a different FPGA. After much study the answer was that the winning candidates were treating the gate logic as analog. Manufacturing flaws or PSU fluctuations would result in the analog aspects behaving differenty. To fix this, they split the fitness test in two passes. All implementations that actually worked got re-run in an emulator, which of course treats the behavior as purely digital. Only if they worked with both did they avoid being culled.
- fho 3y ago
  Iirc there was a somewhat famous case where the design involved some gates that were obviously not connected to the rest of the logic. But if removed the results were different. Iirc the explanation was that the genetic algorithm created an oscillator circuit that became part of the program.
pedrosorio 3y ago
> They claim that instead of a real-world accuracy, they wanted to find the “max” accuracy that their classifier was statistically capable of Yeah, I read this on the GitHub issue a week ago and couldn't believe it. Ideally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Pursuit of truth and knowledge, etc. (1) a young PhD from a prestigious university > For example, I think a neural network is capable of achieving a “max” accuracy of 100% Why reach for such powerful tools? f(x) = random(num_classes), achieves 100% "upper bound" accuracy.
- civilized 3y ago
  If I was confronted with this kind of nonsense in my data science job, I would lose all respect for the person who produced it and never thereafter trust anything they said without thoroughly vetting it. There's only two options here, deceptive or hopelessly incompetent.
- microtonal 3y ago
  Ideally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Academia doesn't have a culture of admitting mistakes. Retracting a paper is typically seen as something shameful rather than progress (by scrutinizing results). Combined with pressure to publish and sometimes limited engineering skills it leads to a volatile mix. There are a lot of published results that are not reproducible when you'd try (not saying anything new here, see replication crisis).
  - dekhn 3y ago
    In its native environment, the scientists reserves its fiercest attacks for its competitors: by fighting tooth and nail, it can render its environment uninhabitable for nearly all but the most determined adversary. Sometimes, this ensures access to desirable mates, but not always.
PaulHoule 3y ago
There are people in the research triangle park area that know stuff little known in Silicon Valley. Aome contractors that were building NLP solutions for three-letter agencies told me the secret to hyper precise systems is to start with a classier that separates easy and hard cases and the create a series of classifiers that can solve the hard cases and fall back on an oracle for the really hard cases. That’s how the original IBM Watson worked.

catgary 3y ago

Of course they won’t admit they made a mistake, they’re watching a career-making paper become a retraction (especially with the training data contamination issues).

deleted 3y ago

[deleted]

ks2048 3y ago

Well put. Yes, I mention a similar case towards the end of that exchange: Consider a random-guess classifier. That has a max accuracy of 100%. Clearly, not a useful measure on its own.

lalaland1125 3y ago

In academia, it's better to cling to obviously false justifications to dismiss criticism and keep a paper accepted than to admit fault and potentially be forced to retract. Publish or perish

bonzini 3y ago
Retracting is extremely rare in computer science, which is why instead many conferences have started "stamping" papers that have artifacts which provide reproducible results.

hinkley 3y ago

A couple of AI hype cycles ago, everyone was abuzz about genetic algorithms. I recall a cautionary tale that was related about someone using FPGAs to do genetic algorithms. After a while they noticed several disturbing things. One, that the winners had fewer gates than theory thought was necessary to solve the problem. Two, some days the winners didn't work, and three, sometimes the winners didn't work on a different FPGA. After much study the answer was that the winning candidates were treating the gate logic as analog. Manufacturing flaws or PSU fluctuations would result in the analog aspects behaving differenty. To fix this, they split the fitness test in two passes. All implementations that actually worked got re-run in an emulator, which of course treats the behavior as purely digital. Only if they worked with both did they avoid being culled.

fho 3y ago
Iirc there was a somewhat famous case where the design involved some gates that were obviously not connected to the rest of the logic. But if removed the results were different. Iirc the explanation was that the genetic algorithm created an oscillator circuit that became part of the program.

pedrosorio 3y ago

> They claim that instead of a real-world accuracy, they wanted to find the “max” accuracy that their classifier was statistically capable of Yeah, I read this on the GitHub issue a week ago and couldn't believe it. Ideally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Pursuit of truth and knowledge, etc. (1) a young PhD from a prestigious university > For example, I think a neural network is capable of achieving a “max” accuracy of 100% Why reach for such powerful tools? f(x) = random(num_classes), achieves 100% "upper bound" accuracy.

civilized 3y ago
If I was confronted with this kind of nonsense in my data science job, I would lose all respect for the person who produced it and never thereafter trust anything they said without thoroughly vetting it. There's only two options here, deceptive or hopelessly incompetent.
microtonal 3y ago
Ideally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Academia doesn't have a culture of admitting mistakes. Retracting a paper is typically seen as something shameful rather than progress (by scrutinizing results). Combined with pressure to publish and sometimes limited engineering skills it leads to a volatile mix. There are a lot of published results that are not reproducible when you'd try (not saying anything new here, see replication crisis).
- dekhn 3y ago
  In its native environment, the scientists reserves its fiercest attacks for its competitors: by fighting tooth and nail, it can render its environment uninhabitable for nearly all but the most determined adversary. Sometimes, this ensures access to desirable mates, but not always.

PaulHoule 3y ago

There are people in the research triangle park area that know stuff little known in Silicon Valley. Aome contractors that were building NLP solutions for three-letter agencies told me the secret to hyper precise systems is to start with a classier that separates easy and hard cases and the create a series of classifiers that can solve the hard cases and fall back on an oracle for the really hard cases. That’s how the original IBM Watson worked.