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Found 4 papers, 1 papers with code
Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods.
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
By coupling this estimate with well-established results in PAC learning theory, we show that a consistent algorithm can recover a $k$-sparse vector with $O(k \lg (n/k))$ measurements, given only the signs of the measurement vector.
Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
It is shown here that SGL achieves robust sparse recovery, and also achieves a version of the grouping effect in that coefficients of highly correlated columns belonging to the same group of the measurement (or design) matrix are assigned roughly comparable values.
Near-Ideal Behavior of Compressed Sensing Algorithms
In a recent paper, it is shown that the LASSO algorithm exhibits "near-ideal behavior," in the following sense: Suppose $y = Az + \eta$ where $A$ satisfies the restricted isometry property (RIP) with a sufficiently small constant, and $\Vert \eta \Vert_2 \leq \epsilon$.
