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Found 6 papers, 0 papers with code
Convex block-sparse linear regression with expanders -- provably
Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.
Learning-based Compressive Subsampling
In this paper, we instead take a principled learning-based approach in which a \emph{fixed} index set is chosen based on a set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$.
Structured Sparsity: Discrete and Convex approaches
For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations.
On Sparsity Inducing Regularization Methods for Machine Learning
During the past years there has been an explosion of interest in learning methods based on sparsity regularization.
Group-Sparse Model Selection: Hardness and Relaxations
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing.
Modelling transition dynamics in MDPs with RKHS embeddings
For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation.
