Trusted AI in multiagent systems: An overview of privacy and security for distributed learning
Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …
as well as the increasing concerns about sharing private data, there has been considerable …
Decentralized task offloading in edge computing: A multi-user multi-armed bandit approach
X Wang, J Ye, JCS Lui - IEEE INFOCOM 2022-IEEE …, 2022 - ieeexplore.ieee.org
Mobile edge computing facilitates users to offload computation tasks to edge servers for
meeting their stringent delay requirements. Previous works mainly explore task offloading …
meeting their stringent delay requirements. Previous works mainly explore task offloading …
Bandit learning in decentralized matching markets
We study two-sided matching markets in which one side of the market (the players) does not
have a priori knowledge about its preferences for the other side (the arms) and is required to …
have a priori knowledge about its preferences for the other side (the arms) and is required to …
Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches
At present, there is a pressing need for data scientists and academic researchers to devise
advanced machine learning and artificial intelligence-driven systems that can effectively …
advanced machine learning and artificial intelligence-driven systems that can effectively …
Heterogeneous multi-player multi-armed bandits: Closing the gap and generalization
Despite the significant interests and many progresses in decentralized multi-player multi-
armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized …
armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized …
Matching in multi-arm bandit with collision
Y Zhang, S Wang, Z Fang - Advances in Neural Information …, 2022 - proceedings.neurips.cc
In this paper, we consider the matching of multi-agent multi-armed bandit problem, ie, while
agents prefer arms with higher expected reward, arms also have preferences on agents. In …
agents prefer arms with higher expected reward, arms also have preferences on agents. In …
Socially fair reinforcement learning
We consider the problem of episodic reinforcement learning where there are multiple
stakeholders with different reward functions. Our goal is to output a policy that is socially fair …
stakeholders with different reward functions. Our goal is to output a policy that is socially fair …
Multitask bandit learning through heterogeneous feedback aggregation
In many real-world applications, multiple agents seek to learn how to perform highly related
yet slightly different tasks in an online bandit learning protocol. We formulate this problem as …
yet slightly different tasks in an online bandit learning protocol. We formulate this problem as …
Decentralized scheduling with qos constraints: Achieving o (1) qos regret of multi-player bandits
We consider a decentralized multi-player multi-armed bandit (MP-MAB) problem where
players cannot observe the actions and rewards of other players and no explicit …
players cannot observe the actions and rewards of other players and no explicit …
Fairness and welfare quantification for regret in multi-armed bandits
We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-
armed bandit (MAB) framework, the current work quantifies the performance of bandit …
armed bandit (MAB) framework, the current work quantifies the performance of bandit …