  The success of neural networks over the past decade has established them as
effective models for many relevant data generating processes. Statistical
theory on neural networks indicates graceful scaling of sample complexity. For
example, Joen & Van Roy (arXiv:2203.00246) demonstrate that, when data is
generated by a ReLU teacher network with $W$ parameters, an optimal learner
needs only $\tilde{O}(W/\epsilon)$ samples to attain expected error $\epsilon$.
However, existing computational theory suggests that, even for
single-hidden-layer teacher networks, to attain small error for all such
teacher networks, the computation required to achieve this sample complexity is
intractable. In this work, we fit single-hidden-layer neural networks to data
generated by single-hidden-layer ReLU teacher networks with parameters drawn
from a natural distribution. We demonstrate that stochastic gradient descent
(SGD) with automated width selection attains small expected error with a number
of samples and total number of queries both nearly linear in the input
dimension and width. This suggests that SGD nearly achieves the
information-theoretic sample complexity bounds of Joen & Van Roy
(arXiv:2203.00246) in a computationally efficient manner. An important
difference between our positive empirical results and the negative theoretical
results is that the latter address worst-case error of deterministic
algorithms, while our analysis centers on expected error of a stochastic
algorithm.

Machine Learning