most citedA General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis

34 citations · 39 across the 8 of their papers we have counts for

collaborators

6 papers

cs.CV2023

Neural Map Prior for Autonomous Driving

Xuan Xiong, Yicheng Liu, Tianyuan Yuan +3

High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. The traditional method of creating offline HD maps involves labor-int…

stat.ML201434 cited

A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis

Yilun Wang, Christine A. Shoemaker

We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima. The r…

stat.ML20141 cited

Sensitivity Analysis for Computationally Expensive Models using Optimization and Objective-oriented Surrogate Approximations

Yilun Wang, Christine A. Shoemaker

In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function in the case that the minimization of it has j…

cs.CV2014

Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification

Yilun Wang, Junjie Zheng, Sheng Zhang +2

In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discrimina…

cs.CV20141 cited

Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection

Yilun Wang, Xinhua Su

Recovering a large matrix from limited measurements is a challenging task arising in many real applications, such as image inpainting, compressive sensing and medical imaging, and…

cs.LG2014

Multi-stage Multi-task feature learning via adaptive threshold

Yaru Fan, Yilun Wang

Multi-task feature learning aims to identity the shared features among tasks to improve generalization. It has been shown that by minimizing non-convex learning models, a better so…