Cooperative stochastic bandits with asynchronous agents and constrained feedback
This paper studies a cooperative multi-armed bandit problem with $ M $ agents cooperating
together to solve the same instance of a $ K $-armed stochastic bandit problem with the goal …
together to solve the same instance of a $ K $-armed stochastic bandit problem with the goal …
Fair exploration via axiomatic bargaining
Motivated by the consideration of fairly sharing the cost of exploration between multiple
groups in learning problems, we develop the Nash bargaining solution in the context of multi …
groups in learning problems, we develop the Nash bargaining solution in the context of multi …
On-demand communication for asynchronous multi-agent bandits
This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where
agents operate asynchronously–agent pull times and rates are unknown, irregular, and …
agents operate asynchronously–agent pull times and rates are unknown, irregular, and …
Exploration for free: how does reward heterogeneity improve regret in cooperative multi-agent bandits?
This paper studies a cooperative multi-agent bandit scenario in which the rewards observed
by agents are heterogeneous—one agent's meat can be another agent's poison …
by agents are heterogeneous—one agent's meat can be another agent's poison …
Cooperative multi-agent bandits: Distributed algorithms with optimal individual regret and constant communication costs
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 …
where a set of distributed agents cooperatively play the same multi-armed bandit game. The …
Adversarial Attacks on Cooperative Multi-agent Bandits
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of
multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities …
multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities …
Human-in-the-loop Learning for Dynamic Congestion Games
Today mobile users learn and share their traffic observations via crowdsourcing platforms
(eg, Google Maps and Waze). Yet such platforms simply cater to selfish users' myopic …
(eg, Google Maps and Waze). Yet such platforms simply cater to selfish users' myopic …
Pure Exploration in Asynchronous Federated Bandits
We study the federated pure exploration problem of multi-armed bandits and linear bandits,
where $ M $ agents cooperatively identify the best arm via communicating with the central …
where $ M $ agents cooperatively identify the best arm via communicating with the central …
Linear Bandits With Side Observations on Networks
We investigate linear bandits in a network setting in the presence of side-observations
across nodes in order to design recommendation algorithms for users connected via social …
across nodes in order to design recommendation algorithms for users connected via social …
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents
Stochastic multi-agent multi-armed bandits typically assume that the rewards from each arm
follow a fixed distribution, regardless of which agent pulls the arm. However, in many real …
follow a fixed distribution, regardless of which agent pulls the arm. However, in many real …