Idan Attias
idanattias.bsky.social
Idan Attias
@idanattias.bsky.social
Postdoc researcher at IDEAL Institute in Chicago, hosted by UIC and TTIC.

My research interests are in machine learning theory, data-driven sequential decision-making, and theoretical computer science.
https://www.idanattias.com/
Link: arxiv.org/abs/2510.04834

Joint work with the super team: Lev Reyzin, Nati Srebro, Gal Vardi
On the Hardness of Learning Regular Expressions
Despite the theoretical significance and wide practical use of regular expressions, the computational complexity of learning them has been largely unexplored. We study the computational hardness of im...
arxiv.org
October 8, 2025 at 2:37 PM
A key takeaway is that what truly matters is the complexity measure (or description length, or equivalently ‘prior’) induced by the model, rather than the concept class itself!
October 8, 2025 at 2:37 PM
We prove hardness in the PAC model and in the membership query setting, under distribution-free learning as well as under the uniform distribution.
Note that DFAs are efficiently learnable with membership queries, whereas we prove that REs remain hard in the same model.
October 8, 2025 at 2:37 PM
The important point is that when we say DFAs or REs are easy or hard to learn, we mean that it is easy or hard to learn languages with *succinct* DFAs or REs. But even though every DFA has an equivalent RE and vice versa, the conversion may require exponential blowups.
October 8, 2025 at 2:37 PM
Thanks Sagnik!
May 18, 2025 at 4:09 PM
💯
March 5, 2025 at 6:21 PM
nice initiative
December 25, 2024 at 5:23 PM
Number one job: keep them alive, the rest is a bonus
December 9, 2024 at 4:31 AM
How about the "set of all CS researcher sets that don't contain themselves"
November 25, 2024 at 4:21 PM
Can you please add me?
November 23, 2024 at 8:10 PM
Thanks, I don't feel grumpy enough
November 22, 2024 at 7:46 PM