A tale of two metrics: simultaneous bounds on competitiveness and regret

Authors:
Lachlan Andrew;Siddharth Barman;Katrina Ligett;Minghong Lin;Adam Meyerson;Alan Roytman;Adam Wierman
Affiliations:
Swinburne University of Technology, Melbourne, Australia;Caltech, Pasadena, USA;Caltech, Pasadena, USA;Caltech, Pasadena, USA;Google Inc., Mountain View, USA;UCLA, Los Angeles, USA;Caltech, Pasadena, USA
Venue:
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Year:
2013

On-line Learning and the Metrical Task System Problem

Machine Learning
Static optimality and dynamic search-optimality in lists and trees

SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Finely-Competitive Paging

FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Logarithmic regret algorithms for online convex optimization

Machine Learning
A decomposition theorem and bounds for randomized server problems

SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Efficient learning algorithms for changing environments

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A regularization approach to metrical task systems

ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
From external to internal regret

COLT'05 Proceedings of the 18th annual conference on Learning Theory

Hi-index	0.00