We consider prediction with expert advice for strongly convex and bounded losses, and investigate trade-offs between regret and “variance” (i.e., squared difference of learner’s predictions and best expert predictions). With K experts, the Exponentially Weighted Average (EWA) algorithm is known to achieve O(logK) regret. We prove that a variant of EWA either achieves a negative regret (i.e., the algorithm outperforms the best expert), or guarantees a O(logK) bound on both variance and regret. Building on this result, we show several examples of how variance of predictions can be exploited in learning. In the online to batch analysis, we show that a large empirical variance allows to stop the online to batch conversion early and outperform the risk of the best predictor in the class. We also recover the optimal rate of model selection aggregation when we do not consider early stopping. In online prediction with corrupted losses, we show that the effect of corruption on the regret can be compensated by a large variance. In online selective sampling, we design an algorithm that samples less when the variance is large, while guaranteeing the optimal regret bound in expectation. In online learning with abstention, we use a similar term as the variance to derive the first high-probability O(logK) regret bound in this setting. Finally, we extend our results to the setting of online linear regression.
A Regret-Variance Trade-Off in Online Learning / D. van der Hoeven, N. Zhivotovskiy, N. Cesa Bianchi (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS). - In: NeurIPS / [a cura di] S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh. - [s.l] : Curran Associates, 2022. - ISBN 9781713871088. - pp. 35188-35200 (( Intervento presentato al 36. convegno Conference on Neural Information Processing Systems : Monday November 28th through Friday December 9th tenutosi a New Orleans nel 2022.
A Regret-Variance Trade-Off in Online Learning
D. van der HoevenPrimo
;N. Cesa BianchiUltimo
2022
Abstract
We consider prediction with expert advice for strongly convex and bounded losses, and investigate trade-offs between regret and “variance” (i.e., squared difference of learner’s predictions and best expert predictions). With K experts, the Exponentially Weighted Average (EWA) algorithm is known to achieve O(logK) regret. We prove that a variant of EWA either achieves a negative regret (i.e., the algorithm outperforms the best expert), or guarantees a O(logK) bound on both variance and regret. Building on this result, we show several examples of how variance of predictions can be exploited in learning. In the online to batch analysis, we show that a large empirical variance allows to stop the online to batch conversion early and outperform the risk of the best predictor in the class. We also recover the optimal rate of model selection aggregation when we do not consider early stopping. In online prediction with corrupted losses, we show that the effect of corruption on the regret can be compensated by a large variance. In online selective sampling, we design an algorithm that samples less when the variance is large, while guaranteeing the optimal regret bound in expectation. In online learning with abstention, we use a similar term as the variance to derive the first high-probability O(logK) regret bound in this setting. Finally, we extend our results to the setting of online linear regression.File | Dimensione | Formato | |
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