\begin{thebibliography}{99}
\raggedright

\bibitem{ruppert1988}
D. Ruppert.
Efficient estimations from a slowly convergent Robbins--Monro process.
Technical Report 781, School of Operations Research and Industrial
Engineering, Cornell University, 1988.
\href{https://ecommons.cornell.edu/entities/publication/9a14790e-66a6-4460-9280-e9fb146fd02d}{Cornell University repository}.

\bibitem{polyak1992}
B.~T. Polyak and A.~B. Juditsky.
Acceleration of stochastic approximation by averaging.
\emph{SIAM Journal on Control and Optimization}, 30(4):838--855, 1992.
\href{https://doi.org/10.1137/0330046}{doi:10.1137/0330046}.

\bibitem{kushner1993}
H.~J. Kushner and J. Yang.
Stochastic approximation with averaging of the iterates: Optimal
asymptotic rate of convergence for general processes.
\emph{SIAM Journal on Control and Optimization}, 31(4):1045--1062, 1993.
\href{https://doi.org/10.1137/0331047}{doi:10.1137/0331047}.

\bibitem{mou2020}
W. Mou, C.~J. Li, M.~J. Wainwright, P.~L. Bartlett, and M.~I. Jordan.
On linear stochastic approximation: Fine-grained Polyak--Ruppert and
non-asymptotic concentration.
In \emph{Proceedings of the Thirty Third Conference on Learning Theory},
volume 125 of \emph{Proceedings of Machine Learning Research},
pages 2947--2997, 2020.
\href{https://proceedings.mlr.press/v125/mou20a.html}{PMLR:125/mou20a}.

\bibitem{konda2004}
V.~R. Konda and J.~N. Tsitsiklis.
Convergence rate of linear two-time-scale stochastic approximation.
\emph{The Annals of Applied Probability}, 14(2):796--819, 2004.
\href{https://doi.org/10.1214/105051604000000116}{doi:10.1214/105051604000000116}.

\bibitem{mokkadem2006}
A. Mokkadem and M. Pelletier.
Convergence rate and averaging of nonlinear two-time-scale stochastic
approximation algorithms.
\emph{The Annals of Applied Probability}, 16(3):1671--1702, 2006.
\href{https://doi.org/10.1214/105051606000000448}{doi:10.1214/105051606000000448}.

\bibitem{mokkadem2011}
A. Mokkadem and M. Pelletier.
A generalization of the averaging procedure: The use of two-time-scale
algorithms.
\emph{SIAM Journal on Control and Optimization}, 49(4):1523--1543, 2011.
\href{https://doi.org/10.1137/080718395}{doi:10.1137/080718395}.

\bibitem{ajalloeian2020}
A. Ajalloeian and S.~U. Stich.
On the convergence of SGD with biased gradients.
\emph{arXiv preprint}, 2020.
\href{https://arxiv.org/abs/2008.00051}{arXiv:2008.00051}.

\bibitem{wang2017}
M. Wang, E.~X. Fang, and H. Liu.
Stochastic compositional gradient descent: Algorithms for minimizing
compositions of expected-value functions.
\emph{Mathematical Programming}, 161(1--2):419--449, 2017.
\href{https://doi.org/10.1007/s10107-016-1017-3}{doi:10.1007/s10107-016-1017-3}.

\bibitem{yu2025}
F. Yu, R. Mehta, A. Luedtke, and Z. Harchaoui.
Stochastic gradients under nuisances.
\emph{Advances in Neural Information Processing Systems}, 2025.
\href{https://arxiv.org/abs/2508.20326}{arXiv:2508.20326}.

\bibitem{robbins1971}
H. Robbins and D. Siegmund.
A convergence theorem for non negative almost supermartingales and some
applications.
In \emph{Optimizing Methods in Statistics}, pages 233--257.
Academic Press, 1971.
\href{https://doi.org/10.1016/B978-0-12-604550-5.50015-8}{doi:10.1016/B978-0-12-604550-5.50015-8}.

\end{thebibliography}