3 papers
cs.LG2026
Efficient Multinomial Logistic Bandit via Frequent Directions
Linzhe He, Yu-Jie Zhang, Sifan Yang +1
This paper studies efficient online algorithms for multinomial logistic bandits (MLogB), where the feedback distribution over outcomes follows a multinomial logistic model of…
cs.LG2026
Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis
Yuheng Zhao, Yu-Hu Yan, Amit Attia +3
Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergence rates competitive with opt…
cs.LG2026
Revisiting Matrix Sketching in Linear Bandits: Achieving Sublinear Regret via Dyadic Block Sketching
Dongxie Wen, Hanyan Yin, Xiao Zhang +3
Linear bandits have become a cornerstone of online learning and sequential decision-making, providing solid theoretical foundations for balancing exploration and exploitation. With…