3 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
Bo Xue, Yimu Wang, Yuanyu Wan +2
This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose -th moment is bounded for some . Although there exist methods…
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Lin Yang, Xuchuang Wang, Mohammad Hajiesmaili +3
Recently, there has been extensive study of cooperative multi-agent multi-armed bandits where a set of distributed agents cooperatively play the same multi-armed bandit game. The g…
Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs
Bo Xue, Guanghui Wang, Yimu Wang +1
In this paper, we study the problem of stochastic linear bandits with finite action sets. Most of existing work assume the payoffs are bounded or sub-Gaussian, which may be violate…