ICML 2017poster0 citations
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
Alina Beygelzimer, Francesco Orabona, Chicheng Zhang
Abstract
We present an efficient second-order algorithm with $\tilde{O}(1/\eta \sqrt{T})$ regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, ranging from hinge loss ($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the open problem of (Abernethy, J. and Rakhlin, A. An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our algorithm experimentally, showing that it performs favorably against earlier algorithms.
BibTeX
@InProceedings{pmlr-v70-beygelzimer17a,
title = {Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret},
author = {Alina Beygelzimer and Francesco Orabona and Chicheng Zhang},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {488--497},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/beygelzimer17a/beygelzimer17a.pdf},
url = {https://proceedings.mlr.press/v70/beygelzimer17a.html},
abstract = {We present an efficient second-order algorithm with $\tilde{O}(1/\eta \sqrt{T})$ regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, ranging from hinge loss ($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the open problem of (Abernethy, J. and Rakhlin, A. An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our algorithm experimentally, showing that it performs favorably against earlier algorithms.}
}