Researchers Introduce Computational Identifiability Framework for Machine Learning
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Users welcome researchers proposing computational identifiability for machine learning because it formalizes everyday search-and-check practices and connects to ideas like computational irreducibility.
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the era of computational math and stat is here: a proof within computational budget and constraints by search rather than a proof under strong assumptions by derivation. along this line of progress, @luciusbynum, rajesh ranganath and yours truly put forward computational identifiability. the link to arxiv below.
preprint at https://arxiv.org/abs/2606.19361
the era of computational math and stat is here: a proof within computational budget and constraints by search rather than a proof under strong assumptions by derivation. along this line of progress, @luciusbynum, rajesh ranganath and yours truly put forward computational identifiability. the link to arxiv below.
@kchonyc @luciusbynum Computational proofs over strong assumptions — this shift feels inevitable as we scale models beyond what classical identifiability can handle.
@kchonyc @luciusbynum This is basically formalizing what everyone already does in practice: run the search and check if you got something reasonable. Makes sense honestly
@kchonyc neat! scanning atm, in context of things like stephen wolfram's computational irreducibility, your identifiabilty also assumes the computational budget will be exhausted in worst case to identify right?