Quantifying the cost of learning in queueing systems
Queueing systems are widely applicable stochastic models with use cases in
communication networks, healthcare, service systems, etc. Although their optimal control …
communication networks, healthcare, service systems, etc. Although their optimal control …
Efficient decentralized multi-agent learning in asymmetric queuing systems
We study decentralized multi-agent learning in bipartite queuing systems, a standard model
for service systems. In particular, N agents request service from K servers in a fully …
for service systems. In particular, N agents request service from K servers in a fully …
Learning to schedule tasks with deadline and throughput constraints
We consider the task scheduling scenario where the controller activates one from K task
types at each time. Each task induces a random completion time, and a reward is obtained …
types at each time. Each task induces a random completion time, and a reward is obtained …
Job dispatching policies for queueing systems with unknown service rates
In multi-server queueing systems where there is no central queue holding all incoming jobs,
job dispatching policies are used to assign incoming jobs to the queue at one of the servers …
job dispatching policies are used to assign incoming jobs to the queue at one of the servers …
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 …
Learning to defer in content moderation: The human-ai interplay
T Lykouris, W Weng - arXiv preprint arXiv:2402.12237, 2024 - arxiv.org
Successful content moderation in online platforms relies on a human-AI collaboration
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …
Bayesian learning of optimal policies in markov decision processes with countably infinite state-space
S Adler, V Subramanian - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Abstract Models of many real-life applications, such as queueing models of communication
networks or computing systems, have a countably infinite state-space. Algorithmic and …
networks or computing systems, have a countably infinite state-space. Algorithmic and …
Gamekeeper: Online learning for admission control of networked open multiagent systems
I Bistritz, N Bambos - IEEE Transactions on Automatic Control, 2024 - ieeexplore.ieee.org
We consider open games where players arrive according to a Poisson process with rate and
stay in the game for an exponential random duration with rate. The game evolves in …
stay in the game for an exponential random duration with rate. The game evolves in …
Learning-based optimal admission control in a single-server queuing system
A Cohen, V Subramanian, Y Zhang - Stochastic Systems, 2024 - pubsonline.informs.org
We consider a long-term average profit–maximizing admission control problem in an M/M/1
queuing system with unknown service and arrival rates. With a fixed reward collected upon …
queuing system with unknown service and arrival rates. With a fixed reward collected upon …
Online task scheduling and termination with throughput constraint
We consider the task scheduling scenario where the controller activates one from task types
at each time. Each task induces a random completion time, and a reward is obtained only …
at each time. Each task induces a random completion time, and a reward is obtained only …