Online Learning with Imperfect Hints

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:822-831, 2020.

Abstract

We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the online player can achieve a regret of $O(\log T)$, thus significantly improving over the $O(\sqrt{T})$ regret in the general setting. However, the result and analysis require the correlation property at \emph{all} time steps, thus raising the natural question: can we design online learning algorithms that are resilient to bad hints? In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case. Our results also generalize, simplify, and improve upon previous results on optimistic regret bounds, which can be viewed as an additive version of hints.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-bhaskara20a, title = {Online Learning with Imperfect Hints}, author = {Bhaskara, Aditya and Cutkosky, Ashok and Kumar, Ravi and Purohit, Manish}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {822--831}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/bhaskara20a/bhaskara20a.pdf}, url = {http://proceedings.mlr.press/v119/bhaskara20a.html}, abstract = {We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the online player can achieve a regret of $O(\log T)$, thus significantly improving over the $O(\sqrt{T})$ regret in the general setting. However, the result and analysis require the correlation property at \emph{all} time steps, thus raising the natural question: can we design online learning algorithms that are resilient to bad hints? In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case. Our results also generalize, simplify, and improve upon previous results on optimistic regret bounds, which can be viewed as an additive version of hints.} }
Endnote
%0 Conference Paper %T Online Learning with Imperfect Hints %A Aditya Bhaskara %A Ashok Cutkosky %A Ravi Kumar %A Manish Purohit %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-bhaskara20a %I PMLR %P 822--831 %U http://proceedings.mlr.press/v119/bhaskara20a.html %V 119 %X We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown that if the hint vector is guaranteed to have a positive correlation with the cost vector, then the online player can achieve a regret of $O(\log T)$, thus significantly improving over the $O(\sqrt{T})$ regret in the general setting. However, the result and analysis require the correlation property at \emph{all} time steps, thus raising the natural question: can we design online learning algorithms that are resilient to bad hints? In this paper we develop algorithms and nearly matching lower bounds for online learning with imperfect hints. Our algorithms are oblivious to the quality of the hints, and the regret bounds interpolate between the always-correlated hints case and the no-hints case. Our results also generalize, simplify, and improve upon previous results on optimistic regret bounds, which can be viewed as an additive version of hints.
APA
Bhaskara, A., Cutkosky, A., Kumar, R. & Purohit, M.. (2020). Online Learning with Imperfect Hints. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:822-831 Available from http://proceedings.mlr.press/v119/bhaskara20a.html.

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