Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent

Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22015-22059, 2022.

Abstract

We aim to make stochastic gradient descent (SGD) adaptive to (i) the noise σ2 in the stochastic gradients and (ii) problem-dependent constants. When minimizing smooth, strongly-convex functions with condition number κ, we prove that T iterations of SGD with exponentially decreasing step-sizes and knowledge of the smoothness can achieve an ˜O(exp(\nicefrac−Tκ)+\nicefracσ2T) rate, without knowing σ2. In order to be adaptive to the smoothness, we use a stochastic line-search (SLS) and show (via upper and lower-bounds) that SGD with SLS converges at the desired rate, but only to a neighbourhood of the solution. On the other hand, we prove that SGD with an offline estimate of the smoothness converges to the minimizer. However, its rate is slowed down proportional to the estimation error. Next, we prove that SGD with Nesterov acceleration and exponential step-sizes (referred to as ASGD) can achieve the near-optimal ˜O(exp(\nicefrac−T√κ)+\nicefracσ2T) rate, without knowledge of σ2. When used with offline estimates of the smoothness and strong-convexity, ASGD still converges to the solution, albeit at a slower rate. Finally, we empirically demonstrate the effectiveness of exponential step-sizes coupled with a novel variant of SLS.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-vaswani22a, title = {Towards Noise-adaptive, Problem-adaptive ({A}ccelerated) Stochastic Gradient Descent}, author = {Vaswani, Sharan and Dubois-Taine, Benjamin and Babanezhad, Reza}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {22015--22059}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/vaswani22a/vaswani22a.pdf}, url = {https://proceedings.mlr.press/v162/vaswani22a.html}, abstract = {We aim to make stochastic gradient descent (SGD) adaptive to (i) the noise $\sigma^2$ in the stochastic gradients and (ii) problem-dependent constants. When minimizing smooth, strongly-convex functions with condition number $\kappa$, we prove that $T$ iterations of SGD with exponentially decreasing step-sizes and knowledge of the smoothness can achieve an $\tilde{O} \left(\exp \left( \nicefrac{-T}{\kappa} \right) + \nicefrac{\sigma^2}{T} \right)$ rate, without knowing $\sigma^2$. In order to be adaptive to the smoothness, we use a stochastic line-search (SLS) and show (via upper and lower-bounds) that SGD with SLS converges at the desired rate, but only to a neighbourhood of the solution. On the other hand, we prove that SGD with an offline estimate of the smoothness converges to the minimizer. However, its rate is slowed down proportional to the estimation error. Next, we prove that SGD with Nesterov acceleration and exponential step-sizes (referred to as ASGD) can achieve the near-optimal $\tilde{O} \left(\exp \left( \nicefrac{-T}{\sqrt{\kappa}} \right) + \nicefrac{\sigma^2}{T} \right)$ rate, without knowledge of $\sigma^2$. When used with offline estimates of the smoothness and strong-convexity, ASGD still converges to the solution, albeit at a slower rate. Finally, we empirically demonstrate the effectiveness of exponential step-sizes coupled with a novel variant of SLS.} }
Endnote
%0 Conference Paper %T Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent %A Sharan Vaswani %A Benjamin Dubois-Taine %A Reza Babanezhad %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-vaswani22a %I PMLR %P 22015--22059 %U https://proceedings.mlr.press/v162/vaswani22a.html %V 162 %X We aim to make stochastic gradient descent (SGD) adaptive to (i) the noise $\sigma^2$ in the stochastic gradients and (ii) problem-dependent constants. When minimizing smooth, strongly-convex functions with condition number $\kappa$, we prove that $T$ iterations of SGD with exponentially decreasing step-sizes and knowledge of the smoothness can achieve an $\tilde{O} \left(\exp \left( \nicefrac{-T}{\kappa} \right) + \nicefrac{\sigma^2}{T} \right)$ rate, without knowing $\sigma^2$. In order to be adaptive to the smoothness, we use a stochastic line-search (SLS) and show (via upper and lower-bounds) that SGD with SLS converges at the desired rate, but only to a neighbourhood of the solution. On the other hand, we prove that SGD with an offline estimate of the smoothness converges to the minimizer. However, its rate is slowed down proportional to the estimation error. Next, we prove that SGD with Nesterov acceleration and exponential step-sizes (referred to as ASGD) can achieve the near-optimal $\tilde{O} \left(\exp \left( \nicefrac{-T}{\sqrt{\kappa}} \right) + \nicefrac{\sigma^2}{T} \right)$ rate, without knowledge of $\sigma^2$. When used with offline estimates of the smoothness and strong-convexity, ASGD still converges to the solution, albeit at a slower rate. Finally, we empirically demonstrate the effectiveness of exponential step-sizes coupled with a novel variant of SLS.
APA
Vaswani, S., Dubois-Taine, B. & Babanezhad, R.. (2022). Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:22015-22059 Available from https://proceedings.mlr.press/v162/vaswani22a.html.

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