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Found 8 papers, 4 papers with code
Testing Stationarity and Change Point Detection in Reinforcement Learning
Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled with existing state-of-the-art RL methods for policy optimization in nonstationary environments.
A Reinforcement Learning Framework for Dynamic Mediation Analysis
However, there are a number of applications (e. g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest.
Doubly Inhomogeneous Reinforcement Learning
Moreover, by borrowing information over time and population, it allows us to detect weaker signals and has better convergence properties when compared to applying the clustering algorithm per time or the change point detection algorithm per subject.
Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity
The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points.
Methodology
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes
A fundamental assumption is that the output/group membership labels for all observations are known.
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
Kernel Deformed Exponential Families for Sparse Continuous Attention
Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.
Dynamic Survival Transformers for Causal Inference with Electronic Health Records
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes.
