Adaptive algorithms for meta-induction

Research output: Contribution to journalArticleResearchpeer-review


Organisational units


Work in online learning traditionally considered induction-friendly (e.g. stochastic with a fixed distribution) and induction-hostile (adversarial) settings separately. While algorithms like Exp3 that have been developed for the adversarial setting are applicable to the stochastic setting as well, the guarantees that can be obtained are usually worse than those that are available for algorithms that are specifically designed for stochastic settings. Only recently, there is an increasing interest in algorithms that give (near-)optimal guarantees with respect to the underlying setting, even in case its nature is unknown to the learner. In this paper, we review various online learning algorithms that are able to adapt to the hardness of the underlying problem setting. While our focus lies on the application of adaptive algorithms as meta-inductive methods that combine given base methods, concerning theoretical properties we are also interested in guarantees that go beyond a comparison to the best fixed base learner.


Original languageEnglish
Number of pages18
JournalJournal for general philosophy of science = Zeitschrift für allgemeine Wissenschaftstheorie
Volume2022 ???
Issue number??? Stand: 30.Dezember 2022
Publication statusE-pub ahead of print - 7 Oct 2022