Efficient Methods for Online Multiclass Logistic Regression

6 October 2021

Papers citing "Efficient Methods for Online Multiclass Logistic Regression"

7 / 7 papers shown

Title
MODL: Multilearner Online Deep Learning Antonios Valkanas Boris N. Oreshkin Mark J. Coates 34 1 0 28 May 2024
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss Shinsaku Sakaue Han Bao Taira Tsuchiya Taihei Oki 30 4 0 13 Feb 2024
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion Junghyun Lee Se-Young Yun Kwang-Sung Jun 14 12 0 28 Oct 2023
High-Probability Risk Bounds via Sequential Predictors Dirk van der Hoeven Nikita Zhivotovskiy Nicolò Cesa-Bianchi OffRL 24 2 0 15 Aug 2023
Quasi-Newton Steps for Efficient Online Exp-Concave Optimization Zakaria Mhammedi Khashayar Gatmiry 14 6 0 02 Nov 2022
Scale-free Unconstrained Online Learning for Curved Losses J. Mayo Hédi Hadiji T. Erven 12 11 0 11 Feb 2022
Mixability made efficient: Fast online multiclass logistic regression Rémi Jézéquel Pierre Gaillard Alessandro Rudi 25 10 0 08 Oct 2021