72 citations · 263 across the 31 of their papers we have counts for
5 papers · 1 filter
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems
Shuaiwen Wang, Wenda Zhou, Arian Maleki +2
Consider the following class of learning schemes: \begin{equation} \label{eq:main-problem1} \hat{\boldsymbolβ} := \underset{\boldsymbolβ \in \mathcal{C}}{\arg\min} \;\sum_{j=1}^n \…
Randomized Gradient Boosting Machine
Haihao Lu, Rahul Mazumder
Gradient Boosting Machine (GBM) introduced by Friedman is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorit…
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Shuaiwen Wang, Wenda Zhou, Haihao Lu +2
Consider the following class of learning schemes: $$\hat{\boldsymbolβ} := \arg\min_{\boldsymbolβ}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbolβ; y_j) + λR(\boldsymbolβ),\qqu…
Generalized Stochastic Frank-Wolfe Algorithm with Stochastic "Substitute" Gradient for Structured Convex Optimization
Haihao Lu, Robert M. Freund
The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a…
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert M. Freund, Vahab Mirrokni
We study ways to accelerate greedy coordinate descent in theory and in practice, where "accelerate" refers either to convergence in theory, in practice, or both. We intr…