  Classical convergence analyses for optimization algorithms rely on the
widely-adopted uniform smoothness assumption. However, recent experimental
studies have demonstrated that many machine learning problems exhibit
non-uniform smoothness, meaning the smoothness factor is a function of the
model parameter instead of a universal constant. In particular, it has been
observed that the smoothness grows with respect to the gradient norm along the
training trajectory. Motivated by this phenomenon, the recently introduced
$(L_0, L_1)$-smoothness is a more general notion, compared to traditional
$L$-smoothness, that captures such positive relationship between smoothness and
gradient norm. Under this type of non-uniform smoothness, existing literature
has designed stochastic first-order algorithms by utilizing gradient clipping
techniques to obtain the optimal $\mathcal{O}(\epsilon^{-3})$ sample complexity
for finding an $\epsilon$-approximate first-order stationary solution.
Nevertheless, the studies of quasi-Newton methods are still lacking.
Considering higher accuracy and more robustness for quasi-Newton methods, in
this paper we propose a fast stochastic quasi-Newton method when there exists
non-uniformity in smoothness. Leveraging gradient clipping and variance
reduction, our algorithm can achieve the best-known
$\mathcal{O}(\epsilon^{-3})$ sample complexity and enjoys convergence speedup
with simple hyperparameter tuning. Our numerical experiments show that our
proposed algorithm outperforms the state-of-the-art approaches.
