activity
20102023
most citedSmoothing Proximal Gradient Method for General Structured Sparse Learning

80 citations · 208 across the 9 of their papers we have counts for

collaborators

8 papers

cs.LG2022

Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization

Yankun Huang, Qihang Lin, Nick Street +1

We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is for…

stat.ML20163 cited

Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing

Xi Chen, Kevin Jiao, Qihang Lin

Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggr…

math.OC2016

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than

Yi Xu, Yan Yan, Qihang Lin +1

In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an ex…

math.OC201438 cited

An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization

Qihang Lin, Zhaosong Lu, Lin Xiao

We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a…

cs.LG201443 cited

Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling

Xi Chen, Qihang Lin, Dengyong Zhou

In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usu…

cs.LG201280 cited

Smoothing Proximal Gradient Method for General Structured Sparse Learning

Xi Chen, Qihang Lin, Seyoung Kim +2

We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input…