Qihang Lin

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 68

Top Topics

Empirical Risk Minimization

Gradient Method

Faster Convergence

Top Venues

J. Mach. Learn. Res.

Publications

Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR (2024)
Yankun Huang, Qihang Lin
Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints. CoRR (2023)
Yankun Huang, Qihang Lin
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization. NeurIPS (2023)
Peijun Qin, Qihang Lin, Yuyang Xie, Yao-Chuan Chang, Stavros Zanos, Hao Wang, Sophie Payne, Mohit N. Shivdasani, David Tsai, Nigel H. Lovell, Socrates Dokos, Tianruo Guo
Modulating functionally-distinct vagus nerve fibers using microelectrodes and kilohertz frequency electrical stimulation. EMBC (2023)
Wei Liu, Qihang Lin, Yangyang Xu
First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods. CoRR (2023)
Qihang Lin, Yangyang Xu
Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method. SIAM J. Optim. 33 (1) (2023)
Yao Yao, Qihang Lin, Tianbao Yang
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS (2023)
Yao Yao, Qihang Lin, Tianbao Yang
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR (2022)
Yao Yao, Qihang Lin, Tianbao Yang
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR (2022)
Qihang Lin, Yangyang Xu
Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems. CoRR (2022)
Yankun Huang, Qihang Lin, W. Nick Street, Stephen Baek
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. CoRR (2022)
Yao Yao, Qihang Lin, Tianbao Yang
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS (2022)
Qihang Lin, Runchao Ma, Yangyang Xu
Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization. Comput. Optim. Appl. 82 (1) (2022)
Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. 37 (3) (2022)
Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. NeurIPS (2022)
Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. CoRR (2022)
Xi Chen, Qihang Lin, Guanglin Xu
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions. INFORMS J. Optim. 4 (1) (2022)
Tong Wang, Qihang Lin
Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. J. Mach. Learn. Res. 22 (2021)
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. 22 (2021)
Runchao Ma, Qihang Lin, Tianbao Yang
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML (2020)
Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu
Bayesian Decision Process for Budget-efficient Crowdsourced Clustering. IJCAI (2020)
Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani
Transparency Promotion with Model-Agnostic Linear Competitors. ICML (2020)
Xi Chen, Qihang Lin, Zizhuo Wang
Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization. INFORMS J. Optim. 2 (1) (2020)
Qihang Lin, Mohit N. Shivdasani, David Tsai, Yao-Chuan Chang, Naveen Jayaprakash, Stavros Zanos, Nigel H. Lovell, Socrates Dokos, Tianruo Guo
A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers. EMBC (2020)
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili
Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage. Manag. Sci. 66 (4) (2020)
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. J. Mach. Learn. Res. 21 (2020)
Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin
Self-guided Approximate Linear Programs. CoRR (2020)
Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR (2020)
Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. Mach. Learn. 109 (5) (2020)
Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS (2020)
Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR (2019)
Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization. J. Mach. Learn. Res. 20 (2019)
Qihang Lin, Runchao Ma, Yangyang Xu
Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints. CoRR (2019)
Tong Wang, Qihang Lin
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. CoRR (2019)
Hassan Rafique, Tong Wang, Qihang Lin
Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models. CoRR (2019)
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR (2019)
Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML (2019)
Michael T. Lash, Qihang Lin, W. Nick Street
Prophit: Causal inverse classification for multiple continuously valued treatment policies. CoRR (2018)
Qihang Lin, Runchao Ma, Tianbao Yang
Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML (2018)
Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR (2018)
Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR (2018)
Qihang Lin, Selvaprabu Nadarajah, Negar Soheili
A Level-Set Method for Convex Optimization with a Feasible Solution Path. SIAM J. Optim. 28 (4) (2018)
Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI (2018)
Tianbao Yang, Qihang Lin
RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. 19 (2018)
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann
Generalized Inverse Classification. SDM (2017)
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS (2017)
Yi Xu, Qihang Lin, Tianbao Yang
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML (2017)
Tianbao Yang, Qihang Lin, Lijun Zhang
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML (2017)
Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. CoRR (2017)
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
A Budget-Constrained Inverse Classification Framework for Smooth Classifiers. ICDM Workshops (2017)
Yi Xu, Qihang Lin, Tianbao Yang
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS (2017)
Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. J. Mach. Learn. Res. 18 (2017)
Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI (2016)
Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS (2016)
Xi Chen, Kevin Jiao, Qihang Lin
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. J. Mach. Learn. Res. 17 (2016)
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann
Generalized Inverse Classification. CoRR (2016)
Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. 103 (1) (2016)
Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
A budget-constrained inverse classification framework for smooth classifiers. CoRR (2016)
Yi Xu, Qihang Lin, Tianbao Yang
Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR (2016)
Xi Chen, Kevin Jiao, Qihang Lin
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. CoRR (2016)
Qihang Lin, Zhaosong Lu, Lin Xiao
An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization. SIAM J. Optim. 25 (4) (2015)
Adams Wei Yu, Qihang Lin, Tianbao Yang
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR (2015)
Tianbao Yang, Qihang Lin, Rong Jin
Big Data Analytics: Optimization and Randomization. KDD (2015)
Xi Chen, Qihang Lin, Dengyong Zhou
Statistical decision making for optimal budget allocation in crowd labeling. J. Mach. Learn. Res. 16 (2015)
Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR (2015)
Qihang Lin, Xi Chen, Javier Peña
A trade execution model under a composite dynamic coherent risk measure. Oper. Res. Lett. 43 (1) (2015)
Jason D. Lee, Tengyu Ma, Qihang Lin
Distributed Stochastic Variance Reduced Gradient Methods. CoRR (2015)
Qihang Lin, Lin Xiao
An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization. Comput. Optim. Appl. 60 (3) (2015)
Xi Chen, Qihang Lin, Dengyong Zhou
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. CoRR (2014)
Qihang Lin, Lin Xiao
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization. ICML (2014)
Qihang Lin, Xi Chen, Javier Peña
A smoothing stochastic gradient method for composite optimization. Optim. Methods Softw. 29 (6) (2014)
Qihang Lin, Xi Chen, Javier Peña
A sparsity preserving stochastic gradient methods for sparse regression. Comput. Optim. Appl. 58 (2) (2014)
Qihang Lin, Zhaosong Lu, Lin Xiao
An Accelerated Proximal Coordinate Gradient Method. NIPS (2014)
Xi Chen, Qihang Lin, Dengyong Zhou
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. ICML (3) (2013)
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing
Smoothing Proximal Gradient Method for General Structured Sparse Learning CoRR (2012)
Xi Chen, Qihang Lin, Javier Peña
Optimal Regularized Dual Averaging Methods for Stochastic Optimization. NIPS (2012)
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing
Smoothing Proximal Gradient Method for General Structured Sparse Learning. UAI (2011)
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell
Sparse Latent Semantic Analysis. SDM (2011)
Xi Chen, Qihang Lin, Seyoung Kim, Javier Peña, Jaime G. Carbonell, Eric P. Xing
An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity CoRR (2010)
Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso CoRR (2010)
Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G. Carbonell
Learning Preferences with Millions of Parameters by Enforcing Sparsity. ICDM (2010)
Qihang Lin
A Smoothing Stochastic Gradient Method for Composite Optimization CoRR (2010)