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Found 8 papers, 1 papers with code
When is exponential asymptotic optimality achievable in average-reward restless bandits?
We show that our policy is asymptotically optimal with an $O(\exp(-C N))$ optimality gap for an $N$-armed problem, under the mild assumptions of aperiodic-unichain, non-degeneracy, and local stability.
Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits
We consider the infinite-horizon, average-reward restless bandit problem in discrete time.
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Unlike homogeneous systems, a threshold policy, that routes jobs to the slow server(s) when the queue length exceeds a certain threshold, is known to be optimal for the one-fast-one-slow two-server system.
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
In both settings, our work is the first asymptotic optimality result that does not require UGAP.
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.
On the Privacy-Utility Tradeoff in Peer-Review Data Analysis
The crux of the framework lies in recognizing that a part of the data pertaining to the reviews is already available in public, and we use this information to post-process the data released by any privacy mechanism in a manner that improves the accuracy (utility) of the data while retaining the privacy guarantees.
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation.
Almost Boltzmann Exploration
In this paper, we show that a simple modification to Boltzmann exploration, motivated by a variation of the standard doubling trick, achieves $O(K\log^{1+\alpha} T)$ regret for a stochastic MAB problem with $K$ arms, where $\alpha>0$ is a parameter of the algorithm.
