Presents a neural network model with IPW pseudo-observations for dynamic prediction of alternating recurrent events, with simulation and medical resident mood application results.
A review on longitudinal data analysis with random forest , volume=
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Fairness-induced exploration in recommenders exhibits diminishing or non-monotonic returns that vary by user interaction history, with low-history users saturating sooner.
The paper introduces neural networks by framing them as approximations to linear regression with common customizations for statisticians.
citing papers explorer
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Dynamic Prediction of Alternating Recurrent Events via Neural Network
Presents a neural network model with IPW pseudo-observations for dynamic prediction of alternating recurrent events, with simulation and medical resident mood application results.
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Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty
Fairness-induced exploration in recommenders exhibits diminishing or non-monotonic returns that vary by user interaction history, with low-history users saturating sooner.
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Neural Networks as Linear Regression: An Introduction for Statisticians
The paper introduces neural networks by framing them as approximations to linear regression with common customizations for statisticians.