Meta-Strategy for Learning Tuning Parameters with Guarantees

Meta-Strategy for Learning Tuning Parameters with Guarantees

Online learning methods, similar to the online gradient algorithm (OGA) and exponentially weighted aggregation (EWA), often depend on tuning parameters that are difficult to set in practice. We consider an online meta-learning scenario, and we propose a meta-strategy to learn these parameters from p...

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Bibliographic Details
Main Authors: Dimitri Meunier, Pierre Alquier
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Entropy
Subjects:
meta-learning
hyperparameters
priors
online learning
Bayesian inference
online optimization
Online Access:https://www.mdpi.com/1099-4300/23/10/1257

Internet

https://www.mdpi.com/1099-4300/23/10/1257

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