A Simple Guard for Learned Optimizers

Isabeau Prémont-Schwarz, Jaroslav Vı́tků, Jan Feyereisl
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17910-17925, 2022.

Abstract

If the trend of learned components eventually outperforming their hand-crafted version continues, learned optimizers will eventually outperform hand-crafted optimizers like SGD or Adam. Even if learned optimizers (L2Os) eventually outpace hand-crafted ones in practice however, they are still not provably convergent and might fail out of distribution. These are the questions addressed here. Currently, learned optimizers frequently outperform generic hand-crafted optimizers (such as gradient descent) at the beginning of learning but they generally plateau after some time while the generic algorithms continue to make progress and often overtake the learned algorithm as Aesop’s tortoise which overtakes the hare. L2Os also still have a difficult time generalizing out of distribution. \cite{heaton_safeguarded_2020} proposed Safeguarded L2O (GL2O) which can take a learned optimizer and safeguard it with a generic learning algorithm so that by conditionally switching between the two, the resulting algorithm is provably convergent. We propose a new class of Safeguarded L2O, called Loss-Guarded L2O (LGL2O), which is both conceptually simpler and computationally less expensive. The guarding mechanism decides solely based on the expected future loss value of both optimizers. Furthermore, we show theoretical proof of LGL2O’s convergence guarantee and empirical results comparing to GL2O and other baselines showing that it combines the best of both L2O and SGD and that in practice converges much better than GL2O.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-premont-schwarz22a, title = {A Simple Guard for Learned Optimizers}, author = {Pr{\'e}mont-Schwarz, Isabeau and V\'{\i}tk{\r{u}}, Jaroslav and Feyereisl, Jan}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {17910--17925}, 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/premont-schwarz22a/premont-schwarz22a.pdf}, url = {https://proceedings.mlr.press/v162/premont-schwarz22a.html}, abstract = {If the trend of learned components eventually outperforming their hand-crafted version continues, learned optimizers will eventually outperform hand-crafted optimizers like SGD or Adam. Even if learned optimizers (L2Os) eventually outpace hand-crafted ones in practice however, they are still not provably convergent and might fail out of distribution. These are the questions addressed here. Currently, learned optimizers frequently outperform generic hand-crafted optimizers (such as gradient descent) at the beginning of learning but they generally plateau after some time while the generic algorithms continue to make progress and often overtake the learned algorithm as Aesop’s tortoise which overtakes the hare. L2Os also still have a difficult time generalizing out of distribution. \cite{heaton_safeguarded_2020} proposed Safeguarded L2O (GL2O) which can take a learned optimizer and safeguard it with a generic learning algorithm so that by conditionally switching between the two, the resulting algorithm is provably convergent. We propose a new class of Safeguarded L2O, called Loss-Guarded L2O (LGL2O), which is both conceptually simpler and computationally less expensive. The guarding mechanism decides solely based on the expected future loss value of both optimizers. Furthermore, we show theoretical proof of LGL2O’s convergence guarantee and empirical results comparing to GL2O and other baselines showing that it combines the best of both L2O and SGD and that in practice converges much better than GL2O.} }
Endnote
%0 Conference Paper %T A Simple Guard for Learned Optimizers %A Isabeau Prémont-Schwarz %A Jaroslav Vı́tků %A Jan Feyereisl %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-premont-schwarz22a %I PMLR %P 17910--17925 %U https://proceedings.mlr.press/v162/premont-schwarz22a.html %V 162 %X If the trend of learned components eventually outperforming their hand-crafted version continues, learned optimizers will eventually outperform hand-crafted optimizers like SGD or Adam. Even if learned optimizers (L2Os) eventually outpace hand-crafted ones in practice however, they are still not provably convergent and might fail out of distribution. These are the questions addressed here. Currently, learned optimizers frequently outperform generic hand-crafted optimizers (such as gradient descent) at the beginning of learning but they generally plateau after some time while the generic algorithms continue to make progress and often overtake the learned algorithm as Aesop’s tortoise which overtakes the hare. L2Os also still have a difficult time generalizing out of distribution. \cite{heaton_safeguarded_2020} proposed Safeguarded L2O (GL2O) which can take a learned optimizer and safeguard it with a generic learning algorithm so that by conditionally switching between the two, the resulting algorithm is provably convergent. We propose a new class of Safeguarded L2O, called Loss-Guarded L2O (LGL2O), which is both conceptually simpler and computationally less expensive. The guarding mechanism decides solely based on the expected future loss value of both optimizers. Furthermore, we show theoretical proof of LGL2O’s convergence guarantee and empirical results comparing to GL2O and other baselines showing that it combines the best of both L2O and SGD and that in practice converges much better than GL2O.
APA
Prémont-Schwarz, I., Vı́tků, J. & Feyereisl, J.. (2022). A Simple Guard for Learned Optimizers. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:17910-17925 Available from https://proceedings.mlr.press/v162/premont-schwarz22a.html.

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