Learning high-dimensional targets by two-parameter models and gradient flow
<|reference_start|>Learning high-dimensional targets by two-parameter models and gradient flow: We explore the theoretical possibility of learning $d$-dimensional targets with $W$-parameter models by gradient flow (GF) when $W<d$. Our main result shows that if the targets are described by a particular $d$-dimensional probability distribution, then there exist models with as few as two parameters that can learn the targets with arbitrarily high success probability. On the other hand, we show that for $W<d$ there is necessarily a large subset of GF-non-learnable targets. In particular, the set of learnable targets is not dense in $\mathbb R^d$, and any subset of $\mathbb R^d$ homeomorphic to the $W$-dimensional sphere contains non-learnable targets. Finally, we observe that the model in our main theorem on almost guaranteed two-parameter learning is constructed using a hierarchical procedure and as a result is not expressible by a single elementary function. We show that this limitation is essential in the sense that such learnability can be ruled out for a large class of elementary functions.<|reference_end|>
@article{yarotsky2024learnability,
    title={Learnability of high-dimensional targets by two-parameter models and
  gradient flow},
    author={Dmitry Yarotsky},
    journal={arXiv preprint arXiv:2402.17089},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17089},
    primaryClass={stat.ML cs.LG}
}