Variable Metric Stochastic Approximation Theory

Peter Sunehag, Jochen Trumpf, S.V.N. Vishwanathan, Nicol Schraudolph
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, PMLR 5:560-566, 2009.

Abstract

We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results in the areas of elicitation of properties of distributions using prediction markets and in learning from expert advice.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-sunehag09a, title = {Variable Metric Stochastic Approximation Theory}, author = {Sunehag, Peter and Trumpf, Jochen and Vishwanathan, S.V.N. and Schraudolph, Nicol}, booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics}, pages = {560--566}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/sunehag09a/sunehag09a.pdf}, url = {https://proceedings.mlr.press/v5/sunehag09a.html}, abstract = {We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results in the areas of elicitation of properties of distributions using prediction markets and in learning from expert advice.} }
Endnote
%0 Conference Paper %T Variable Metric Stochastic Approximation Theory %A Peter Sunehag %A Jochen Trumpf %A S.V.N. Vishwanathan %A Nicol Schraudolph %B Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-sunehag09a %I PMLR %P 560--566 %U https://proceedings.mlr.press/v5/sunehag09a.html %V 5 %X We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results in the areas of elicitation of properties of distributions using prediction markets and in learning from expert advice.
RIS
TY - CPAPER TI - Variable Metric Stochastic Approximation Theory AU - Peter Sunehag AU - Jochen Trumpf AU - S.V.N. Vishwanathan AU - Nicol Schraudolph BT - Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-sunehag09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 560 EP - 566 L1 - http://proceedings.mlr.press/v5/sunehag09a/sunehag09a.pdf UR - https://proceedings.mlr.press/v5/sunehag09a.html AB - We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results in the areas of elicitation of properties of distributions using prediction markets and in learning from expert advice. ER -
APA
Sunehag, P., Trumpf, J., Vishwanathan, S. & Schraudolph, N.. (2009). Variable Metric Stochastic Approximation Theory. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:560-566 Available from https://proceedings.mlr.press/v5/sunehag09a.html.

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