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Found 11 papers, 1 papers with code
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch.
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks
We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i. e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server.
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints
Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints.
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework.
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
Most research for this problem focuses exclusively on the settings that players have \textit{full access} to all arms and receive no reward when pulling the same arm.
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation
Using this structural result, we establish the indexability of our problem, and employ the Whittle index policy to minimize average latency.
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
We call it the R(MA)^2B-UCB algorithm.
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset.
Learning Augmented Index Policy for Optimal Service Placement at the Network Edge
We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of customers that are currently waiting at the edge to obtain the service.
