From Undecidability of Non-Triviality and Finiteness to Undecidability of Learnability

TL;DR

This paper proves that determining the learnability of models in various machine learning frameworks is fundamentally undecidable, highlighting inherent limitations in automating model evaluation.

Contribution

It establishes the undecidability of learnability in key learning paradigms by encoding classical undecidable problems into the evaluation of model complexity and triviality.

Findings

Learnability is undecidable in PAC, online, and exact learning.

No general algorithm can determine if a model will successfully learn.

Undecidability stems from encoding halting and consistency problems.

Abstract

Machine learning researchers and practitioners steadily enlarge the multitude of successful learning models. They achieve this through in-depth theoretical analyses and experiential heuristics. However, there is no known general-purpose procedure for rigorously evaluating whether newly proposed models indeed successfully learn from data. We show that such a procedure cannot exist. For PAC binary classification, uniform and universal online learning, and exact learning through teacher-learner interactions, learnability is in general undecidable, both in the sense of independence of the axioms in a formal system and in the sense of uncomputability. Our proofs proceed via computable constructions that encode the consistency problem for formal systems and the halting problem for Turing machines into whether certain function classes are trivial/finite or highly complex, which we then relate to whether these classes are learnable via established characterizations of learnability through complexity measures. Our work shows that undecidability appears in the theoretical foundations of artificial intelligence: There is no one-size-fits-all algorithm for deciding whether a machine learning model can be successful. We cannot in general automatize the process of assessing new learning models.