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 …
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 …
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 …
Learning while scheduling in multi-server systems with unknown statistics: Maxweight with discounted ucb
Multi-server queueing systems are widely used models for job scheduling in machine
learning, wireless networks, and crowdsourcing. This paper considers a multi-server system …
learning, wireless networks, and crowdsourcing. This paper considers a multi-server system …
Experimenting under stochastic congestion
We study randomized experiments in a service system when stochastic congestion can arise
from temporarily limited supply and/or demand. Such congestion gives rise to cross-unit …
from temporarily limited supply and/or demand. Such congestion gives rise to cross-unit …
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 …
Queue scheduling with adversarial bandit learning
In this paper, we study scheduling of a queueing system with zero knowledge of
instantaneous network conditions. We consider a one-hop single-server queueing system …
instantaneous network conditions. We consider a one-hop single-server queueing system …
Mean-field analysis for load balancing on spatial graphs
D Rutten, D Mukherjee - Abstract Proceedings of the 2023 ACM …, 2023 - dl.acm.org
A pivotal methodological tool behind the analysis of large-scale load balancing systems is
mean-field analysis. The high-level idea is to represent the system state by aggregate …
mean-field analysis. The high-level idea is to represent the system state by aggregate …