How do we measure and compare prediction quality in a symbolic/deterministic context that also punish for complexity? I struggled on this in my phd thesis and had to brew my own Minimum Description Length implementation because I found nothing applicable. The statisticians I interrogated were like "don't try to do model selection on symbolic models, make them probabilistic and work from there"
Nice! I wrote an SR engine in Rust for SRBench, happy to see the field getting some front-page attention.
Repo: https://github.com/owls-on-wires/keplearn
SR Bench Results: https://github.com/cavalab/srbench/pull/223
How do we measure and compare prediction quality in a symbolic/deterministic context that also punish for complexity? I struggled on this in my phd thesis and had to brew my own Minimum Description Length implementation because I found nothing applicable. The statisticians I interrogated were like "don't try to do model selection on symbolic models, make them probabilistic and work from there"
Is the Julia part here compiled via JuliaC and if not what would it take to do so?
Has anyone thrown the n-body problem at this?
related: https://github.com/verdverm/pge-jax
A deterministic algorithm for Symbolic Regression, which I recently ported to JAX
weird that my comment time was updated, this was not today
(original post refreshed by HN mods if I understand correctly, maybe dang or tomhow will chime in on this)
Second chance pool. All comments get a new timestamp with the submission for a while. Eventually it will have its original timestamp.
Yay now when you load your python project you have 2 interpreters, 2 gcs, 200mb of ram overhead, and nondeterministic performance due to joat.