We propose a new high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients, subject to a budget on the maximal absolute correlation between the features and residuals. We show that the estimator can be expressed as a solution to a Mixed Integer Linear Optimization (MILO) problem, a computationally tractable framewo...

The Dantzig selector performs variable selection and model fitting in linear regression. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, they tend to over-shrink the final coefficients. This results in an unfortunate trade-off. One ...

The Dantzig Selector is a recent approach to estimation in high-dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contra...

The acquisition of a specific cellular identity is usually paralleled by a restriction of cellular plasticity. Whether and how these two processes are coordinated is poorly understood. Transcription factors called terminal selectors activate identity-specific effector genes during neuronal differentiation to define the structural and functional properties of a neuron. To study restriction of pl...

Which samples should be labelled in a large dataset is one of the most important problems for training deep learning. So far, variety active sample selection strategies related to learning have been proposed literature. We defined them as Active Deep Learning (ADL) only if their predictor or selector model, where basic learner called and labeling schemes are selector. In this survey, we categor...

The selection of peptides for presentation at the surface of most nucleated cells by major histocompatibility complex class I molecules (MHC I) is crucial to the immune response in vertebrates. However, the mechanisms of the rapid selection of high affinity peptides by MHC I from amongst thousands of mostly low affinity peptides are not well understood. We developed computational systems models...