Improved strongly adaptive online learning using coin betting

Kwang Sung Jun; Francesco Orabona; Stephen Wright; Rebecca Willett

Improved strongly adaptive online learning using coin betting

Kwang Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √log(T) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

Original language	English (US)
Title of host publication	Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Publisher	PMLR
State	Published - Jan 1 2017
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25

Cite this

@inproceedings{da623964459c4b96b40623e224ebf59d,

title = "Improved strongly adaptive online learning using coin betting",

abstract = "This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √log(T) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.",

author = "Jun, {Kwang Sung} and Francesco Orabona and Stephen Wright and Rebecca Willett",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-25",

year = "2017",

month = jan,

day = "1",

language = "English (US)",

booktitle = "Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017",

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T1 - Improved strongly adaptive online learning using coin betting

AU - Jun, Kwang Sung

AU - Orabona, Francesco

AU - Wright, Stephen

AU - Willett, Rebecca

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-25

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √log(T) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

AB - This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least √log(T) better, where T is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

UR - http://www.scopus.com/inward/record.url?scp=85083937416&partnerID=8YFLogxK

M3 - Conference contribution

BT - Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017

PB - PMLR

ER -

Improved strongly adaptive online learning using coin betting

Abstract

Bibliographical note

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