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Found 6 papers, 0 papers with code
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention
Motivated by the emerging needs of personalized preventative intervention in many healthcare applications, we consider a multi-stage, dynamic decision-making problem in the online setting with unknown model parameters.
Group-Aware Threshold Adaptation for Fair Classification
As we only need an estimated probability distribution of model output instead of the classification model structure, our post-processing model can be applied to a wide range of classification models and improve fairness in a model-agnostic manner and ensure privacy.
A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations
To address the issue, we propose a novel two-level routing component to the queueing network model.
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
Thus, the overall computational complexity of our algorithm is similar to that of the linear UCB for unconstrained stochastic linear bandits.
POND: Pessimistic-Optimistic oNline Dispatching
This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions.
Robustness-Driven Exploration with Probabilistic Metric Temporal Logic
However, algorithms for autonomous exploration often focus on optimizing time and coverage in a greedy fashion.
