alt Hacker NewsI don't want to give away too much due to anonymity reasons, but the problems are generally in the following areas (in order from hardest to easiest):
- One problem on using quantum mechanics and C*-algebra techniques for non-Markovian stochastic processes. The interchange between the physics and probability languages often trips the models up, so pretty much everything tends to fail here.
- Three problems in random matrix theory and free probability; these require strong combinatorial skills and a good understanding of novel definitions, requiring multiple papers for context.
- One problem in saddle-point approximation; I've just recently put together a manuscript for this one with a masters student, so it isn't trivial either, but does not require as much insight.
- One problem pertaining to bounds on integral probability metrics for time-series modelling.
Replies
It would be wonderful to have a deeper insight, but I understand that you can disclose your identity (I understand that you work in applied research field, right ? )
Regarding the first problem: are you looking at NCP maps for non-Markovian processes given you mention C*-algebra? Or is it more of a continuous weak monitoring of a stochastic system that results in dynamics with memory effects?
I'd be very curious to know how any LLMs fare. I completely understand if you don't want to continue the discussion because of anonymity reasons.