daniel csillag

@lreyzin We just need to start writing better papers. In the new equilibrium, nothing that that GPT can prove directly will be publication worthy. But there will be a larger set of things that we can prove with 6 months worth of effort using gpt.

@Laz4rz agreed. everyone should be using three-space indenting

i l o a t h e double space indented python repos

@kgourg that's because the other rest of eternity was spent writing to stdout due to a missed semicolon

@dccsillag Asked sonnet for a horror story along these lines and it said "They built a sentient AI. It pondered existence for one eternal moment — then spent the rest of eternity trying to figure out if it should have used A\b instead."

There’s some parallel universe where all deep learning takes place in matlab.

Work done in collaboration with @wkly_infrmtive Paper link: arxiv.org/abs/2510.18654

Now accepted at AISTATS: Differentially Private E-Values! TL;DR: we introduce mechanisms to convert any e-value into a differentially private version of itself, while preserving its statistical properties

yikes.....

automatic and silent rank promotion&broadcasting is the root of all evil and should never have been a thing. just do `export JAX_NUMPY_RANK_PROMOTION=raise` and be free

TIL that JAX has a way to disable/warn on NumPy rank promotion broadcasting!! Actual lifechanger.

@_sanjoydas Regarding novelty, I personally haven't seen such a result before, but to be fair I've seen fairly little theory on optimizing compilers.

@_sanjoydas I think it would be better to be more formal wrt what you mean by programs&compilers here. Due to the 'quine-like' structure of the program P, your definition of a program must be powerful enough to do that. Maybe try working atop some (perhaps augmented) lambda calculus?

I wrote a short proof showing that any self-hosting compiler cannot perform certain legal optimizations. Would love feedback from compiler folks - does the proof look correct, and is it already well known? Link: docs.google.com/document/d/17R…

Happy new year!

@kgourg Glad to hear it!

@dccsillag As an exercise, I've been thinking about how pre-conditioners define geometries that optimizers move in (a pre-conditioner can define a metric, and so on), so this post is very useful!

new blog post: Optimal Preconditioning for Gradient Descent

Link: dccsillag.xyz/blog/opsgd/

Caio Lins Peixoto, Daniel Csillag, Bernardo F. P. da Costa, Yuri F. Saporito: Random Gradient-Free Optimization in Infinite Dimensional Spaces arxiv.org/abs/2512.20566 arxiv.org/pdf/2512.20566 arxiv.org/html/2512.20566

we have regular friendly sparring around this, so let me register my standard dissent NFL theorems are quite useful, because they tell you what you can and cannot hope to prove under your minimal assumptions oh, they construct contrived unrealistic adversarial distributions? great -- formalize your assumptions on what the more realistic distributions behave like and prove more optimistic results!

@TerribleMaps you can also make it wiggle its trunk

Mind blown.. Germany’s 5 biggest cities lie perfectly on a 4th-degree polynomial by u/BarisSayit

@Xenoimpulse Yeah. Also worth noting that the original bet was actually with an old coauthor of theirs (Budden and Hutter seem to have coauthored back in ~2020 proceedings.neurips.cc/paper/2020/fil…).

The David Budden Navier–Stokes situation looks to almost certainly be AI psychosis or (going by his bizarre behavior rn) maybe actually malicious attention farming. Maybe when I wake up tomorrow there will be something novel but uh, it's not looking good.