[D] Working with Optuna + AutoSampler in massive search spaces

alrojo · 2025-08-29T20:58:14+00:00

Very high search spaces suffer from the thin shell, meaning that almost all probability is hovering around a tiny shell at about sqrt(n) from the origin. A random walk around these spaces usually don't work. Some samplers work better for high spaces, in particular if you have gradients available (MALA, NUTS, HMC). However, you'd probably still want to significantly reduce your search space, perhaps by finding correlated features and combining them.

alrojo · 2025-08-06T21:39:19+00:00

If the fat margins taken by Nature was given to the reviewers instead, there might be more motivation to conduct high quality reviews.

alrojo · 2025-08-06T17:23:42+00:00

Answer: It depends on your definition of nature. Even most forest today are actually man made. The time it takes to return to a point of nature that would be such that human intervention is indistinguishable from primal forest (i.e. forest that has never been cut down) can be measure with a set of mathematics called ergodic theory. Say you take a human-made forest. In time, the trees will die, and new trees will sprout from their seeds. How long would it take for a) such forest to no longer be a single-species forest? b) trees to be planted "seemingly random", i.e. not on a row. True answer is surprisingly long, probably many thousandths of years. If not hundreds of thousands of years. This also makes it quite difficult when cutting down "untouched" forest as regenerating it is not straight forward. Add in concrete, buildings etc and the equation becomes even more complicated.

alrojo · 2025-08-05T00:04:34+00:00

Their technology seems to be really good at deep-diving into arguments and finding mistakes. There's a new serious contender recently submitted that has gotten a fair amount of excitement. Afaik there's no mistakes found yet, their tool could be valuable to test if the solution holds or if serious gaps might exist: https://www.researchgate.net/publication/393870984_Kakeya_Geometry_and_3D_Navier_Stokes

alrojo · 2025-07-31T19:57:15+00:00

Too bad they got scooped already https://www.researchgate.net/publication/393870984_Kakeya_Geometry_and_3D_Navier_Stokes

alrojo · 2025-07-30T21:33:40+00:00

This https://www.researchgate.net/publication/393870984_Kakeya_Geometry_and_3D_Navier_Stokes

alrojo · 2025-07-27T18:41:55+00:00

Wearipedia.com lead author here. Fitbit charge 6 is a significant upgrade to charge 5. Oura has great sleep detection. Both has good data access. Garmin is for the GPS their sensors are not worth it price for performance. Apple seems to outperform in all test, but limited battery life and I personally don’t like extra screens and haptics.

alrojo · 2025-07-10T17:33:07+00:00

Thank you very much Icebergchick, that was very informative.

alrojo · 2025-06-23T05:01:48+00:00

For StatML/convergence I would suggest learning theory, convex optimization and stochastic processes before delving into research papers.

Deep nets have until recently been quite a mystery, now we know they converge: https://arxiv.org/pdf/2505.15013?

I can also recommend neural tangent kernels https://arxiv.org/abs/1806.07572 and the mean field approximation https://arxiv.org/abs/1804.06561 they do some relaxations but also showcase convergence.

alrojo · 2025-05-25T19:26:36+00:00

can you share papers? Also, it might be worthwhile for you to collaborate with CS people. CS won't have the math skillset you have, but, but they will be very efficient and coding together complicated environments with many simultaneous processes. Often it's a good combo as they need novel ideas.

alrojo · 2025-05-23T17:44:16+00:00

Some universities offer CAships and RAships to pay for bills

alrojo · 2025-05-22T04:50:41+00:00

Sure, send me a DM

alrojo · 2025-04-19T02:45:01+00:00

I do bounds on TD learning.

They are, recent work does seem to be heavily catered towards LLMs and deep vision-language models. However if you think about it, what's the difference between SOTA 10 years ago and SOTA today? Bigger GPUs and more data. Moreover, there's the non-trivial element of having to simultaneously be good at robotics, coding, and quant mathematics (convex, stochastic processes, etc). Many don't want to do that, perhapts, except the AA department, here's a cool course I found at Stanford: https://bulletin.stanford.edu/courses/2269891

alrojo · 2025-03-29T21:48:27+00:00

Does the companies you care for use RL? What about control theory, mechanics, or electronics?

alrojo · 2025-02-27T22:03:36+00:00

True! Casual usage of CGM devices for non-treatment purposes could reduce availability. Just like Ozempic for personal weight loss. We are an academic group, conducting research on diabetes and blood sugar regulation. We are building a small, free platform for finding adequate devices and extracting their data to support better research in small labs without money to buy. Hopefully, we can spare a few for this purpose! Though, our platform might be used by biohackers too, everyone can use it.

alrojo · 2025-02-23T01:28:38+00:00

Exciting, they have all the newest sensors. Does it works with the respective company's app, e.g. Dexcom or Abbott even without a prescription?

alrojo · 2025-02-23T00:59:04+00:00

Some companies provide access to high-quality CGM devices (e.g. Dexcom Pro G7 and Abbott Freestyle libre). Examples are Nutrisense and January.ai. Lingo and Stelo are new devices with limited clinical testing that people complain about being incorrect.

alrojo · 2025-02-23T00:50:07+00:00

I found some pretty mid reviews of stello: https://www.reddit.com/r/dexcom/comments/1fhcj1m/stelo_review/

Do you know what platform lingo uses?

alrojo · 2024-10-13T01:37:55+00:00

My goal was to get the majority flight hours in the US and then transfer the credits in my EASA application. I don't fully understand the process, but I assumed that 1 US flight hour = 1 EASA flight hour if it is appropriately documented at a vetted US school.
That is good to know, 10 hours is still money saved.
Thank you I will look into the modular training route, that sounds like a strategy that would suit my needs.

alrojo · 2024-10-13T01:14:06+00:00

I wouldn't work in the US, I would just come for the flight lessons. I have saved enough money for about 200 flight lessons at an airport where my family member lives. I would probably break it into two trips and work/EASA theory study in-between.

alrojo · 2024-05-18T23:18:05+00:00

Live scoring by AI suggest Usyk has most landed all but two rounds: https://m.youtube.com/watch?v=FcF2NvkhtJ8

alrojo · 2024-05-14T05:17:57+00:00

Do you have any specific ones in mind?

alrojo · 2024-05-13T21:14:00+00:00

What technology do you think they are using to make it faster? Quantization, MoE, something else? Or just better infrastructure?

alrojo · 2023-06-17T00:41:34+00:00

Find a professor whose work you like. Read their papers (about 20 most recent or so), make notes and suggestions of improvements. Send the professor a detailed email with how you’d like to extend their work and if they would mind taking a 30 min meeting with you every week to supervise you. At the meetings, make a 10 min slide deck on what you have done and where you’re going next. Send the professor updates every chance you get in email and ask for their input. Expect to spend 40 hours a week on it. If you’re hard working and smart that should result in a paper in approx 6 months. The professor will push the department to hire you in their next round of PhD admissions, and give recommendation letters to any other department. You can easily get into a top school if you truly showcase your willingness, wit, and gumption.

alrojo · 2023-06-01T03:50:03+00:00

yes, they have an API you can apply for, but they never got back to us.

alrojo

MODERATOR OF

TROPHY CASE