Optimistically Tempered Online Learning
Optimistic Online Learning algorithms have been developed to exploit expert advices, assumed optimistically to be always useful. However, it is legitimate to question the relevance of such advices \emph{w.r.t.} the learning information provided by gradient-based online algorithms. In this work, we challenge the confidence assumption on the expert and develop the \emph{optimistically tempered} (OT) online learning framework as well as OT adaptations of online algorithms. Our algorithms come with sound theoretical guarantees in the form of dynamic regret bounds, and we eventually provide experimental validation of the usefulness of the OT approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Theory of Optimistically Universal Online Learnability for General Concept Classes
We provide a full characterization of the concept classes that are optimistically universally online learnable with $\{0, 1\}$ labels. The notion of optimistically universal online learning was defined in [Hanneke, 2021]…
PhilosophyUniversal Online Learning: an Optimistically Universal Learning Rule
We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of an universally consistent learning was defined by Hanneke in an effort to study learning theory under minimal …
Learning TheoryMemorizationUniversal Online Learning with Unbounded Losses: Memory Is All You Need
We resolve an open problem of Hanneke on the subject of universally consistent online learning with non-i.i.d. processes and unbounded losses. The notion of an optimistically universal learning rule was defined by Hannek…
AllLearning TheoryMemorizationThe Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs
Tempered Exponential Measures (TEMs) are a parametric generalization of the exponential family of distributions maximizing the tempered entropy function among positive measures subject to a probability normalization of t…
Semantic segmentation of SEM images of lower bainitic and tempered martensitic steels
This study employs deep learning techniques to segment scanning electron microscope images, enabling a quantitative analysis of carbide precipitates in lower bainite and tempered martensite steels with comparable strengt…
Deep LearningSemantic Segmentation