Abstract | ||
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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. |
Year | Venue | DocType |
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2020 | ICML | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Corinna Cortes | 1 | 6574 | 1120.50 |
Giulia DeSalvo | 2 | 73 | 6.45 |
Claudio Gentile | 3 | 1166 | 107.46 |
Mehryar Mohri | 4 | 4502 | 448.21 |
Ningshan Zhang | 5 | 4 | 3.72 |