Regret Bounds for Log-Loss via Bayesian Algorithms-Reference-Cited by-同舟云学术

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Regret Bounds for Log-Loss via Bayesian Algorithms

Published:2023-09 Issue:9 Volume:69 Page:5971-5989
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Wu Changlong¹^ORCID,Heidari Mohsen¹^ORCID,Grama Ananth¹^ORCID,Szpankowski Wojciech¹^ORCID

Affiliation:

1. Center for Science of Information (CSoI), West Lafayette, IN, USA

Funder

NSF Center for Science of Information

NSF

Google Research Grant

Rolls-Royce

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/10225323/10131912-aam.pdf

Reference52 articles.

1. Precise minimax regret for logistic regression with categorical feature values;jacquet;Proceedings Algorithmic Learning Theory (ALT),2021

2. Sequential vs. Fixed Design Regrets in Online Learning

3. Online learning: Random averages, combinatorial parameters, and learnability;rakhlin;Proc Adv Neural Inf Process Syst (NIPS),2010

4. Universal sequential coding of single messages;shtarkov;Problems Inf Transmiss,1987

5. Expected worst case regret via stochastic sequential covering;wu;arXiv 2209 04417,2022

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