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Found 6 papers, 1 papers with code
Boosting with Multiple Sources
We study the problem of learning accurate ensemble predictors, in particular boosting, in the presence of multiple source domains.
Agnostic Learning with Multiple Objectives
We further implement the algorithm in a popular symbolic gradient computation framework and empirically demonstrate on a number of datasets the benefits of $\almo$ framework versus learning with a fixed mixture weights distribution.
Regularized Gradient Boosting
We fill this gap by deriving data-dependent learning guarantees for \GB\ used with \emph{regularization}, expressed in terms of the Rademacher complexities of the constrained families of base predictors.
Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
In this paper, we design efficient gradient computation algorithms for two broad families of structured prediction loss functions: rational and tropical losses.
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.
Foundations of Coupled Nonlinear Dimensionality Reduction
The generalization error bound is based on a careful analysis of the empirical Rademacher complexity of the relevant hypothesis set.
