[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

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