Publications (27)
Generalized Inverse Classification
Michael T. Lash, Qihang Lin, W. Nick Street +2
On Degrees of Freedom of Projection Estimators with Applications to Multivariate Nonparametric Regression
Xi Chen, Qihang Lin, Bodhisattva Sen
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Michael T. Lash, Qihang Lin, W. Nick Street
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Lin Xiao, Adams Wei Yu, Qihang Lin +1
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin +2
A budget-constrained inverse classification framework for smooth classifiers
Michael T. Lash, Qihang Lin, W. Nick Street +1
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees
Tianbao Yang, Lijun Zhang, Qihang Lin +1
Smoothing proximal gradient method for general structured sparse regression
Xi Chen, Qihang Lin, Seyoung Kim +2
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Tong Wang, Qihang Lin
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity
Jason D. Lee, Qihang Lin, Tengyu Ma +1
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
Tianbao Yang, Qihang Lin
An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization
Qihang Lin, Zhaosong Lu, Lin Xiao
Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization
Xi Chen, Qihang Lin, Zizhuo Wang
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
Xi Chen, Qihang Lin, Dengyong Zhou
Smoothing Proximal Gradient Method for General Structured Sparse Learning
Xi Chen, Qihang Lin, Seyoung Kim +2
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem
Adams Wei Yu, Qihang Lin, Tianbao Yang
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Xi Chen, Kevin Jiao, Qihang Lin
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
Jianhui Chen, Tianbao Yang, Qihang Lin +2
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
Tianbao Yang, Qihang Lin, Lijun Zhang
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso
Xi Chen, Seyoung Kim, Qihang Lin +2
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions
Xi Chen, Qihang Lin, Guanglin Xu
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Yan Yan, Tianbao Yang, Zhe Li +2
Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network
Adams Wei Yu, Lei Huang, Qihang Lin +2
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$
Yi Xu, Yan Yan, Qihang Lin +1
Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
Tianbao Yang, Qihang Lin, Zhe Li
Stochastic subGradient Methods with Linear Convergence for Polyhedral Convex Optimization
Tianbao Yang, Qihang Lin
A Smoothing Stochastic Gradient Method for Composite Optimization
Qihang Lin, Xi Chen, Javier Pena