4 papers
Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
William de Vazelhes, Xiao-Tong Yuan, Bin Gu
In sparse optimization, enforcing hard constraints using the pseudo-norm offers advantages like controlled sparsity compared to convex relaxations. However, many real-worl…
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
Hilal AlQuabeh, William de Vazelhes, Bin Gu
Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximizat…
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
William de Vazelhes, Bhaskar Mukhoty, Xiao-Tong Yuan +1
Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive…
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory
Hilal AlQuabeh, Bhaskar Mukhoty, Bin Gu
Pairwise learning is essential in machine learning, especially for problems involving loss functions defined on pairs of training examples. Online gradient descent (OGD) algorithms…