Online Learning with Dependent Stochastic Feedback Graphs

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2154-2163, 2020.

Abstract

A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner. We study a challenging scenario where feedback graphs vary stochastically with time and, more importantly, where graphs and losses are dependent. This scenario appears in several real-world applications that we describe where the outcome of actions are correlated. We devise a new algorithm for this setting that exploits the stochastic properties of the graphs and that benefits from favorable regret guarantees. We present a detailed theoretical analysis of this algorithm, and also report the result of a series of experiments on real-world datasets, which show that our algorithm outperforms standard baselines for online learning with feedback graphs.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-cortes20b, title = {Online Learning with Dependent Stochastic Feedback Graphs}, author = {Cortes, Corinna and Desalvo, Giulia and Gentile, Claudio and Mohri, Mehryar and Zhang, Ningshan}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2154--2163}, 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/cortes20b/cortes20b.pdf}, url = {http://proceedings.mlr.press/v119/cortes20b.html}, abstract = {A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner. We study a challenging scenario where feedback graphs vary stochastically with time and, more importantly, where graphs and losses are dependent. This scenario appears in several real-world applications that we describe where the outcome of actions are correlated. We devise a new algorithm for this setting that exploits the stochastic properties of the graphs and that benefits from favorable regret guarantees. We present a detailed theoretical analysis of this algorithm, and also report the result of a series of experiments on real-world datasets, which show that our algorithm outperforms standard baselines for online learning with feedback graphs.} }
Endnote
%0 Conference Paper %T Online Learning with Dependent Stochastic Feedback Graphs %A Corinna Cortes %A Giulia Desalvo %A Claudio Gentile %A Mehryar Mohri %A Ningshan Zhang %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-cortes20b %I PMLR %P 2154--2163 %U http://proceedings.mlr.press/v119/cortes20b.html %V 119 %X A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner. We study a challenging scenario where feedback graphs vary stochastically with time and, more importantly, where graphs and losses are dependent. This scenario appears in several real-world applications that we describe where the outcome of actions are correlated. We devise a new algorithm for this setting that exploits the stochastic properties of the graphs and that benefits from favorable regret guarantees. We present a detailed theoretical analysis of this algorithm, and also report the result of a series of experiments on real-world datasets, which show that our algorithm outperforms standard baselines for online learning with feedback graphs.
APA
Cortes, C., Desalvo, G., Gentile, C., Mohri, M. & Zhang, N.. (2020). Online Learning with Dependent Stochastic Feedback Graphs. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2154-2163 Available from http://proceedings.mlr.press/v119/cortes20b.html.

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