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Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design.
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach
With identifiability of the network guaranteed, this paper presents an identification method that infers both the Boolean structure of the network and the internal dynamics between nodes.
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations
Nonnegative matrix factorization (NMF) is a popular dimension reduction technique that produces interpretable decomposition of the data into parts.
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