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Efficient improper learning for online logistic regression

Efficient improper learning for online logistic regression

18 March 2020
Rémi Jézéquel
Pierre Gaillard
Alessandro Rudi
ArXivPDFHTML

Papers citing "Efficient improper learning for online logistic regression"

4 / 4 papers shown
Title
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Junghyun Lee
Se-Young Yun
Kwang-Sung Jun
30
4
0
19 Jul 2024
High-Probability Risk Bounds via Sequential Predictors
High-Probability Risk Bounds via Sequential Predictors
Dirk van der Hoeven
Nikita Zhivotovskiy
Nicolò Cesa-Bianchi
OffRL
34
2
0
15 Aug 2023
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Louis Faury
Marc Abeille
Kwang-Sung Jun
Clément Calauzènes
22
19
0
06 Jan 2022
Efficient Methods for Online Multiclass Logistic Regression
Efficient Methods for Online Multiclass Logistic Regression
Naman Agarwal
Satyen Kale
Julian Zimmert
24
9
0
06 Oct 2021
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