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Found 6 papers, 1 papers with code
Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization
The Frank-Wolfe method has become increasingly useful in statistical and machine learning applications, due to the structure-inducing properties of the iterates, and especially in settings where linear minimization over the feasible set is more computationally efficient than projection.
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
We propose a novel Stochastic Frank-Wolfe (a. k. a.
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods
When the training data is non-separable, we show that the degree of non-separability naturally enters the analysis and informs the properties and convergence guarantees of two standard first-order methods: steepest descent (for any given norm) and stochastic gradient descent.
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion
Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region.
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Furthermore, we show that these new algorithms for the Lasso may also be interpreted as the same master algorithm (subgradient descent), applied to a regularized version of the maximum absolute correlation loss function.
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods
Boosting methods are highly popular and effective supervised learning methods which combine weak learners into a single accurate model with good statistical performance.
