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"
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"
The approach here assigns a cost to each operator, variable and constant. See docs.
related: https://github.com/owls-on-wires/keplearn
A deterministic SR engine in Rust, written for SRBench. Happy to see the field getting some front-page attention!
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.