gregoryfaletto.com
Statistician, Data Scientist.
261 posts
272 followers
612 following
Active Commenter
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When did the topic change to how one can win an election?
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Many mostly middle-aged and old white people who watch Fox really didn't like Biden. Show us the day that 2% of the population turned out to protest him?
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Now if the researchers subsequently collected completely independent data to "validate" the green jelly bean hypothesis, that would of course be perfectly fine
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Yes, the fundamental problem is failing to correct for multiple hypothesis testing. The sense in which one could frame the issue as "timing" is if the researcher pretends only after seeing the p-values that actually they were really only interested in green jelly beans all along
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Well here's one example of what people are talking about when they say HARKing is bad
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The status quo where anybody who has any business publishing can submit to the most prestigious journals in their field, have a good chance of getting reasonable feedback from experts who otherwise might not have read the work, and maybe get published if it's warranted gives new names an opportunity
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Nice paper! You touch on related points a bit in S3, but I'm concerned that eliminating peer review takes away one of the few pathways new researchers have to make good work prominent without either being attached to an already-prominent researcher or being good at "marketing."
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I found this video super helpful, this was actually the first thing that made TMLE click for me. The meat of it is in the second half www.youtube.com/watch?v=8Q9d...
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would agree if it were june 2023, but why don't they have anything more baked by now?
it blew my mind how long it took autocomplete in messages to noticeably improve. no application of LLMs could be more straightforward than literally predicting the next token
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Yes and unit economics will start to become more of the ballgame than they already are, especially if "reliably replacing a human end-to-end" is looking unachievable for the most part
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Yeah reasoning is kind of the biggest lift in the last couple of years I guess. I guess we'll see as really huge models get trained, maybe scaling laws will keep up... maybe they won't
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Yeah we'll see what happens, maybe a year from now we'll look back on current capabilities and feel like they're way worse. But when I look back at say last fall, the improvements feel noticeably slower than the improvements in the 9 months before that.
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(People keep saying "you can code things without even having to know how to code!" and that keeps... not really being true. Can code in languages that you don't really know if you're already a coder, sure.)
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One thing I've been thinking about: let's say the transformer architecture kind of plateaus at a point where they're v useful if you have pro-level knowledge in your field (coding etc.), but can't reliably replace a human unsupervised on end-to-end tasks. Could be a pretty nice sweet spot for labor
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Which model?
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Let's at least all be clear about who we're arguing about deserving more sympathy.
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I wish we'd acknowledge more that the biggest "losers" in the 2022 - 2024 economy was about the 70th - 90th percentile of income, who experienced rapid wage growth in the bottom quartile of earners as price increases. (chart is unfairly negative IMO since includes 2020, but couldn't find better one)
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Citizens have a civic duty to vote for whichever of the major-party candidates they think would be a better leader. Not doing so is a moral failing, and it's good for the country to discourage it. If the candidate you dislike less doesn't win, then the candidate you dislike more will.
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Vanilla Least Squares (VLS)
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Pretty unlikely it happened today
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It is. Try it yourself right now
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And I didn't bring up the topic, but I also think it's kind of not okay for news outlets to prioritize this topic (which it really feels like we have already gotten to the bottom of) over harm being done in the present that elected officials have the power to stop, but don't.
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I just disagree that the criticism is "proportional" in any reasonable sense. I guess my starting point for thinking about proportions is units like lives lost that can be directly tied to a specific policy choice, number of people wrongfully detained, number of laws broken, etc.
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Biden's age issues were the main news topic for almost a full month after the June 27th debate. There were certainly many other days where it was the leading story. In your view, how much additional criticism is needed to be proportional?
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The "why" is potentially more complicated depending on how deep you want to get into "why." (Looking at the system prompt would probably help.)
But "what" is going on is perfectly clear, and IMO the less we lose sight of that (and the more the general public understands that) the better
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You push specific dough through this special machine, you get noodles. You push specific strings through those matrices, you get some text. Both machines can be quite useful for certain purposes
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Big numbers are getting pushed through a bunch of very particular matrix multiplications
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realizing I'm way behind for the day, making myself 9 full bowls of plain spaghetti, then interlacing my fingers and cracking my knuckles as I prepare to get started
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A personal assistant who is 80% as smart as you and can get stuff done while you get a glass of water is a massively useful tool
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Not that specific one, but some of these posts seem photoshopped, or at the very least not reproducible. When I see these posts sometimes I'll try the exact query on the exact model and get back a sensible response
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Let he who has never typed a function from memory and then realized they misremembered the exact name cast the first stone
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something I think about all the time is how many people there are whose only voluntary interaction with LLMs was trying whatever free OpenAI model was available months/years ago and getting frustrated
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That's very helpful, thank you!
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It's very important that we either forbid developers from making bad investments that would lose them money or forbid them from investments that would make them too much money, depending on which one of those would result in housing getting built
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☹️
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I can invert a 3 x 3 matrix by hand, doesn't mean it makes sense for me to actually do it!
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middle manager is one of the most underrated jobs. it is hard, people who are really good at it are rare, if you could snap your fingers and make every middle manager twice as good the world would be noticeably better.
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Yes, and precise positioning of text, images etc. using the "arrange" tab on the right side of the screen
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Coriolis hive activated
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To me it's like, sure calculators shouldn't be allowed on all tests, but it would also be insane to insist not only that kids should never learn how to use calculators, it's in fact shameful if they ever did
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It's kind of fascinating to me that some people think this is obviously cheating 100% of the time and there's never a place for it. These kids are going to graduate and the ones who didn't learn how to "cheat" like this are going to have employers who are pissed off at them for it.
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if you have a pressure cooker, this is really good. i substituted tempeh for mushrooms cooking.nytimes.com/recipes/1021...
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I think the difference to the argument is that instead of this term disappearing asymptotically because it's mean 0, it just wouldn't appear at all
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Indeed, and I believe the same argument in the post works for ANCOVA I too
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And how much will the resulting estimator look like vanilla ANCOVA II?
It turns out they're asymptotically equivalent--makes sense since they have the same asymptotic properties.
In this blog post, I walk through the math & supporting simulations.
Check it out!
gregoryfaletto.com/2025/05/03/e...
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So here's a question: what if we use ANCOVA II as the outcome model in a cross-fit AIPW estimator?
Since treatment assignment is randomized, the propensity scores are known--errors are already o(1/√n).
So even if ANCOVA II is misspecified, we should get consistency and asymptotic normality.
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If we use the AIPW estimator with cross-fitting, Theorem 5.1 of Chernozhukov et al (2018) guarantees consistent and asymptotically normal estimation of the ATE under unconfoundedness and overlap if the product of the rates of convergence of the propensity and outcome models are o(1/√n).
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Another setting where we can get consistent and asymptotically normal estimation of the ATE with a misspecified model is the doubly-robust AIPW estimator.
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In randomized experiments, the OLS coefficient on the treatment dummy is a consistent and asymptotically normal estimator for the average treatment effect (ATE).
This holds regardless of whether the linearity assumption is true.