Robustness and scalability under heavy tails, without strong convexity

Matthew Holland
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:865-873, 2021.

Abstract

Real-world data is laden with outlying values. The challenge for machine learning is that the learner typically has no prior knowledge of whether the feedback it receives (losses, gradients, etc.) will be heavy-tailed or not. In this work, we study a simple, cost-efficient algorithmic strategy that can be leveraged when both losses and gradients can be heavy-tailed. The core technique introduces a simple robust validation sub-routine, which is used to boost the confidence of inexpensive gradient-based sub-processes. Compared with recent robust gradient descent methods from the literature, dimension dependence (both risk bounds and cost) is substantially improved, without relying upon strong convexity or expensive per-step robustification. We also empirically show that the proposed procedure cannot simply be replaced with naive cross-validation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-holland21a, title = { Robustness and scalability under heavy tails, without strong convexity }, author = {Holland, Matthew}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {865--873}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/holland21a/holland21a.pdf}, url = {https://proceedings.mlr.press/v130/holland21a.html}, abstract = { Real-world data is laden with outlying values. The challenge for machine learning is that the learner typically has no prior knowledge of whether the feedback it receives (losses, gradients, etc.) will be heavy-tailed or not. In this work, we study a simple, cost-efficient algorithmic strategy that can be leveraged when both losses and gradients can be heavy-tailed. The core technique introduces a simple robust validation sub-routine, which is used to boost the confidence of inexpensive gradient-based sub-processes. Compared with recent robust gradient descent methods from the literature, dimension dependence (both risk bounds and cost) is substantially improved, without relying upon strong convexity or expensive per-step robustification. We also empirically show that the proposed procedure cannot simply be replaced with naive cross-validation. } }
Endnote
%0 Conference Paper %T Robustness and scalability under heavy tails, without strong convexity %A Matthew Holland %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-holland21a %I PMLR %P 865--873 %U https://proceedings.mlr.press/v130/holland21a.html %V 130 %X Real-world data is laden with outlying values. The challenge for machine learning is that the learner typically has no prior knowledge of whether the feedback it receives (losses, gradients, etc.) will be heavy-tailed or not. In this work, we study a simple, cost-efficient algorithmic strategy that can be leveraged when both losses and gradients can be heavy-tailed. The core technique introduces a simple robust validation sub-routine, which is used to boost the confidence of inexpensive gradient-based sub-processes. Compared with recent robust gradient descent methods from the literature, dimension dependence (both risk bounds and cost) is substantially improved, without relying upon strong convexity or expensive per-step robustification. We also empirically show that the proposed procedure cannot simply be replaced with naive cross-validation.
APA
Holland, M.. (2021). Robustness and scalability under heavy tails, without strong convexity . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:865-873 Available from https://proceedings.mlr.press/v130/holland21a.html.

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