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Linear Bandits with Feature Feedback
In comparison, the regret of traditional linear bandits is $d\sqrt{T}$, where $d$ is the total number of (relevant and irrelevant) features, so the improvement can be dramatic if $k\ll d$.
Scalable Sparse Subspace Clustering via Ordered Weighted $\ell_1$ Regression
The main contribution of the paper is a new approach to subspace clustering that is significantly more computationally efficient and scalable than existing state-of-the-art methods.
Block CUR: Decomposing Matrices using Groups of Columns
In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix.
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis.
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