TY - GEN

T1 - Fast Margin Maximization via Dual Acceleration

AU - Ji, Ziwei

AU - Srebro, Nathan

AU - Telgarsky, Matus

N1 - Publisher Copyright:
Copyright © 2021 by the author(s)

PY - 2021

Y1 - 2021

N2 - We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of Õ(1/t2). This contrasts with a rate of O(1/log(t)) for standard gradient descent, and O(1/t) for normalized gradient descent. This momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive nonuniform sampling via the dual variables.

AB - We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of Õ(1/t2). This contrasts with a rate of O(1/log(t)) for standard gradient descent, and O(1/t) for normalized gradient descent. This momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive nonuniform sampling via the dual variables.

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M3 - Conference contribution

AN - SCOPUS:85161314065

T3 - Proceedings of Machine Learning Research

SP - 4860

EP - 4869

BT - Proceedings of the 38th International Conference on Machine Learning, ICML 2021

PB - ML Research Press

T2 - 38th International Conference on Machine Learning, ICML 2021

Y2 - 18 July 2021 through 24 July 2021

ER -