31 citations · 31 across the 5 of their papers we have counts for
5 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…
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
Chengqian Gao, William de Vazelhes, Hualin Zhang +2
Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably,…
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…
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
William de Vazelhes, Hualin Zhang, Huimin Wu +2
constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Har…
metric-learn: Metric Learning Algorithms in Python
William de Vazelhes, CJ Carey, Yuan Tang +2
metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unif…