Minimax Algorithm for Learning Rotations

Wojciech Kotłowski, Manfred K. Warmuth
Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:821-824, 2011.

Abstract

It is unknown what is the most suitable regularization for rotation matrices and how to maintain uncertainty over rotations in an online setting. We propose to address these questions by studying the minimax algorithm for rotations and begin by working out the 2-dimensional case.

Cite this Paper

BibTeX
@InProceedings{pmlr-v19-kotlowski11b, title = {Minimax Algorithm for Learning Rotations}, author = {Kotłowski, Wojciech and Warmuth, Manfred K.}, booktitle = {Proceedings of the 24th Annual Conference on Learning Theory}, pages = {821--824}, year = {2011}, editor = {Kakade, Sham M. and von Luxburg, Ulrike}, volume = {19}, series = {Proceedings of Machine Learning Research}, address = {Budapest, Hungary}, month = {09--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v19/kotlowski11b/kotlowski11b.pdf}, url = {https://proceedings.mlr.press/v19/kotlowski11b.html}, abstract = {It is unknown what is the most suitable regularization for rotation matrices and how to maintain uncertainty over rotations in an online setting. We propose to address these questions by studying the minimax algorithm for rotations and begin by working out the 2-dimensional case.} }
Endnote
%0 Conference Paper %T Minimax Algorithm for Learning Rotations %A Wojciech Kotłowski %A Manfred K. Warmuth %B Proceedings of the 24th Annual Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2011 %E Sham M. Kakade %E Ulrike von Luxburg %F pmlr-v19-kotlowski11b %I PMLR %P 821--824 %U https://proceedings.mlr.press/v19/kotlowski11b.html %V 19 %X It is unknown what is the most suitable regularization for rotation matrices and how to maintain uncertainty over rotations in an online setting. We propose to address these questions by studying the minimax algorithm for rotations and begin by working out the 2-dimensional case.
RIS
TY - CPAPER TI - Minimax Algorithm for Learning Rotations AU - Wojciech Kotłowski AU - Manfred K. Warmuth BT - Proceedings of the 24th Annual Conference on Learning Theory DA - 2011/12/21 ED - Sham M. Kakade ED - Ulrike von Luxburg ID - pmlr-v19-kotlowski11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 19 SP - 821 EP - 824 L1 - http://proceedings.mlr.press/v19/kotlowski11b/kotlowski11b.pdf UR - https://proceedings.mlr.press/v19/kotlowski11b.html AB - It is unknown what is the most suitable regularization for rotation matrices and how to maintain uncertainty over rotations in an online setting. We propose to address these questions by studying the minimax algorithm for rotations and begin by working out the 2-dimensional case. ER -
APA
Kotłowski, W. & Warmuth, M.K.. (2011). Minimax Algorithm for Learning Rotations. Proceedings of the 24th Annual Conference on Learning Theory, in Proceedings of Machine Learning Research 19:821-824 Available from https://proceedings.mlr.press/v19/kotlowski11b.html.

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