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Found 4 papers, 4 papers with code
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.
Automatic Change-Point Detection in Time Series via Deep Learning
We show how to automatically generate new offline detection methods based on training a neural network.
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.
Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm
We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series.
Change Point Detection
Model Selection
+1
Methodology
