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Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding of this problem for non-differentiable penalties such as generalized LASSO and nuclear norm is missing.
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers (extended version)
Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with differentiable regularizers.
TopRank+: A Refinement of TopRank Algorithm
Online learning to rank is a core problem in machine learning.
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