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Found 7 papers, 2 papers with code
Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification
Our approach addresses the computational challenges inherent in traditional optimization-based algorithms by harnessing deep learning models to predict optimal interference management solutions.
Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning
Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks.
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy
Conventional approaches for dynamic scheduling find the optimal policy for a given specific system so that the policy from these approaches is usable only for the corresponding system characteristics.
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning
To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL.
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.
Contextual Constrained Learning for Dose-Finding Clinical Trials
In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.
