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Found 5 papers, 2 papers with code
Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration
$m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty.
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.
Deep reinforcement learning for large-scale epidemic control
For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza.
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers
In general, our approach outperforms PointNet on every family of 3D geometries on which the models were tested.
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i. e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature.
