3 citations · 6 across the 4 of their papers we have counts for
4 papers
Regret Lower Bounds in Multi-agent Multi-armed Bandit
Mengfan Xu, Diego Klabjan
Multi-armed Bandit motivates methods with provable upper bounds on regret and also the counterpart lower bounds have been extensively studied in this context. Recently, Multi-agent…
Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards
Mengfan Xu, Diego Klabjan
We study a decentralized multi-agent multi-armed bandit problem in which multiple clients are connected by time dependent random graphs provided by an environment. The reward distr…
Pareto Regret Analyses in Multi-objective Multi-armed Bandit
Mengfan Xu, Diego Klabjan
We study Pareto optimality in multi-objective multi-armed bandit by providing a formulation of adversarial multi-objective multi-armed bandit and defining its Pareto regrets that c…
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
Mengfan Xu, Diego Klabjan
We study the challenging exploration incentive problem in both bandit and reinforcement learning, where the rewards are scale-free and potentially unbounded, driven by real-world s…