On Asymptotics of Optimal Stopping Times [PDF]
Mathematics, 2022 We consider optimal stopping problems, in which a sequence of independent random variables is drawn from a known continuous density. The objective of such problems is to find a procedure which maximizes the expected reward.
Hugh N. Entwistle +2 more
doaj +2 more sources
Bayesian Response-Adaptive Randomization for Cluster Randomized Controlled Trials. [PDF]
Stat MedCluster randomized controlled trials where groups (or clusters) of individuals, rather than single individuals, are randomized are especially useful when individual‐level randomization is not feasible or when interventions are naturally delivered at the ...
Liu Y, Young Karris M, Jain S.
europepmc +2 more sources
Learning Security Strategies through Game Play and Optimal Stopping [PDF]
arXiv.org, 2022 We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve through ...
K. Hammar, R. Stadler
semanticscholar +1 more source
Intrusion Prevention Through Optimal Stopping [PDF]
IEEE Transactions on Network and Service Management, 2021 We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping problem.
K. Hammar, R. Stadler
semanticscholar +1 more source
Learning Near-Optimal Intrusion Responses Against Dynamic Attackers [PDF]
IEEE Transactions on Network and Service Management, 2023 We study automated intrusion response and formulate the interaction between an attacker and a defender as an optimal stopping game where attack and defense strategies evolve through reinforcement learning and self-play.
K. Hammar, R. Stadler
semanticscholar +1 more source
Automatic Evaluation of Neural Network Training Results
Computers, 2023 This article is dedicated to solving the problem of an insufficient degree of automation of artificial neural network training. Despite the availability of a large number of libraries for training neural networks, machine learning engineers often have to
Roman Barinov +3 more
doaj +1 more source
Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Unknown Delay Statistics [PDF]
J. Commun. Networks, 2023 In this paper, we consider sampling an Ornstein-Uhlenbeck (OU) process through a channel for remote estimation. The goal is to minimize the mean square error (MSE) at the estimator under a sampling frequency constraint when the channel delay statistics ...
Yuchao Chen +4 more
semanticscholar +1 more source
Sampling of the Wiener Process for Remote Estimation Over a Channel With Unknown Delay Statistics [PDF]
IEEE/ACM Transactions on Networking, 2022 In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling
Haoyue Tang, Yin Sun, L. Tassiulas
semanticscholar +1 more source
Optimal Adaptation for Early Stopping in Statistical Inverse Problems [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2018 abridged and corrected ...
Blanchard, Gilles +2 more
openaire +4 more sources
Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting
IET Software, 2023 Corona Virus disease 2019 (COVID‐19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non‐pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or ...
Boyu Huang +6 more
doaj +1 more source