Stochastic Dual Coordinate Ascent with Adaptive Probabilities

Dominik Csiba, Zheng Qu, Peter Richtarik
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:674-683, 2015.

Abstract

This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSDCA achieves provably better complexity bound than SDCA with the best fixed probability distribution, known as importance sampling. However, it is of a theoretical character as it is expensive to implement. We also propose AdaSDCA+: a practical variant which in our experiments outperforms existing non-adaptive methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-csiba15, title = {Stochastic Dual Coordinate Ascent with Adaptive Probabilities}, author = {Csiba, Dominik and Qu, Zheng and Richtarik, Peter}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {674--683}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/csiba15.pdf}, url = {https://proceedings.mlr.press/v37/csiba15.html}, abstract = {This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSDCA achieves provably better complexity bound than SDCA with the best fixed probability distribution, known as importance sampling. However, it is of a theoretical character as it is expensive to implement. We also propose AdaSDCA+: a practical variant which in our experiments outperforms existing non-adaptive methods.} }
Endnote
%0 Conference Paper %T Stochastic Dual Coordinate Ascent with Adaptive Probabilities %A Dominik Csiba %A Zheng Qu %A Peter Richtarik %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-csiba15 %I PMLR %P 674--683 %U https://proceedings.mlr.press/v37/csiba15.html %V 37 %X This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSDCA achieves provably better complexity bound than SDCA with the best fixed probability distribution, known as importance sampling. However, it is of a theoretical character as it is expensive to implement. We also propose AdaSDCA+: a practical variant which in our experiments outperforms existing non-adaptive methods.
RIS
TY - CPAPER TI - Stochastic Dual Coordinate Ascent with Adaptive Probabilities AU - Dominik Csiba AU - Zheng Qu AU - Peter Richtarik BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-csiba15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 674 EP - 683 L1 - http://proceedings.mlr.press/v37/csiba15.pdf UR - https://proceedings.mlr.press/v37/csiba15.html AB - This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent (SDCA) for solving the regularized empirical risk minimization problems. Our modification consists in allowing the method adaptively change the probability distribution over the dual variables throughout the iterative process. AdaSDCA achieves provably better complexity bound than SDCA with the best fixed probability distribution, known as importance sampling. However, it is of a theoretical character as it is expensive to implement. We also propose AdaSDCA+: a practical variant which in our experiments outperforms existing non-adaptive methods. ER -
APA
Csiba, D., Qu, Z. & Richtarik, P.. (2015). Stochastic Dual Coordinate Ascent with Adaptive Probabilities. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:674-683 Available from https://proceedings.mlr.press/v37/csiba15.html.

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