Dan Roy

Dan Roy

@roydanroy.bsky.social

Research Director, Founding Faculty, Canada CIFAR AI Chair @VectorInst. Full Prof @UofT - Statistics and Computer Sci. (x-appt) danroy.org I study assumption-free prediction and decision making under uncertainty, with inference emerging from optimality.

8.5K followers552 following154 posts

Tian and Karolina and team are at ICLR. Come say hi.

Tian Jin@tjin.bsky.social

📣 The Journey Matters: Our #ICLR2025 paper shows how to pretrain sparse LLMs with half the size of dense LLMs while maintaining quality. We found that the average parameter count during sparse pre-training predicts quality, not final size. An MIT/Rice/Google/ISTA collab 🧵 1/N

Someone has suggested I check out bsky again. So I'm back looking around here. Notification list is kinda boring. So any good conversations going on? Perhaps about LLM/AI reasoning?

I got to ski Revelstoke this winter break. Couple observations: the price of receiving 600 cm of snow by Jan 8 is that it is constantly snowing. Saw almost no sun the whole time and the peak was often in whiteout conditions (though North Bowl was always clear…). See image for more.

Multiple friends have likely lost their homes in Los Angeles. Can’t imagine how disorienting this would be. They had only minutes to flee and grab belongings.

Now that I've had a taste of X without post length limitations, I've got to say that it is quite annoying have to fit tweets into 256 characters here on bsky. On X, when they get to long, they go below the fold, and so you're still incentivized to make it short. Can't we have that here?

Has anyone out there compared the GPU/memory specs for the new Macbook Pro M4 Pro/Max computers to the requirements to run inference in the more interesting open source LLM models, especially those aimed at reasoning? [1/2]

What I'm curious about is whether there's a compelling case to spring for a Macbook Pro M4 or one of the Pro/Max model from the perspective of being able to do "inference" in one of the more interesting models on the laptop itself, without having to deal with an external machine/cloud, etc. [2/3]

Dan Roy reposted

Huge community response to this. Much appreciated.

Dan Roy@roydanroy.bsky.social

My 9 yo has finished all the Harry Potters. Any suggestion on where to go from here? Maybe age appropriate sci fi?

NeurIPS Test of Time Awards: Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le

Waterloo is facing a 75 million CAD deficit and so they are implementing a hiring freeze. As one of the world’s top universities, it is ridiculous that Ontario is not funding it fully. It has generated so much SV wealth, I suggest a SV billionaire solve this deficit and then some.

Dan Roy reposted

Hot take: Working another weekend this semester, I am left wondering: Why does the #NLProc publication process have so much artificial urgency baked into it? Requiring responses within 24h, emergency reviewers, everything urgent. Like, why? Slow science can also be a community goal.

Has anyone asked whether scaling laws are surprising or not? (Not only to meat puppets, but surprising in the real sense of "it could have been different".) Universal learning rates look like scaling laws in the non-extreme case, where the class isn't trivial or impossible. No compute there though.

In 1975, he described the construction of what has come to be known as the "Loeb measure". It has led to important applications in probability theory, potential theory, number theory, mathematical economics and mathematical physics. [2/3]

The Loeb measure connects probability measures in nonstandard model of analysis to standard probability measures. It allows one to, e.g., create a (hyper)finite model of Brownian motion (due to Anderson).

Hey... anyone know w.t.f. runs BlueSky? Does this sit on top of mastodon? Can we edit the software? How do we tweak our feeds? E.g., if I'm in the mood for ML or for politics or whatever?

Reviewing is dead. I just read some ICLR reviews. Absolutely junk. No insight. Written by humans, but could have been written by a chatbot or teenager who had read a few dozen reviews. Why do we think this is useful? Only 5% (if that!) of the community has worthwhile opinions.

Getting some pushback. Right now, reviewing is an activity we do not even prioritize. We force people to do it, with no ~0 repercussions for doing a crappy job. No one gets tenure by reviewing well, or spending their time shooting down junk. Once we fix _that_, we will get useful peer review.

Demis talks about AlphaGo choosing the optimal on a board with 10^170 positions but we already know that AlphaGo was very suboptimal. Which raises the question of how we might ever know how good AlphaGo is in any absolute sense.

By demand, I've created the final starter pack in my ML Personality Starter Pack Series. I'm uncertain who belongs in this starter pack and so if you think you better fit in the Grumpy ML or Unreasonably Upbeat ML starter packs, let me know. (Self) nominations welcome go.bsky.app/5Suyk58

Any lighting nerds out there? I'm curious about the possibility of replacing (some of) my kitchen--dining room lights with (something like) "full spectrum" lights to fight lack of sun / winter blues. I have a lightbox from back in my days in Boston. Looking to exploit physiological effects.

I'm surprised this result (which is essentially a nonuniform version realizable, online classification) was not known. Result says you can make a finite number of mistakes if and only if the class is a countable union of classes with finite Littlestone dimension. arxiv.org/abs/2312.00170

arxiv.orgWhen Is Inductive Inference Possible?Can a physicist make only a finite number of errors in the eternal quest to uncover the law of nature? This millennium-old philosophical problem, known as inductive inference, lies at the heart of epi...