Lin, Qihang

38 publications

TMLR 2026 Single-Loop Algorithms for Stochastic Non-Convex Optimization with Weakly-Convex Constraints Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang

JMLR 2025 An Adaptive Parameter-Free and Projection-Free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition Qihang Lin, Negar Soheili, Runchao Ma, Selvaprabu Nadarajah

TMLR 2025 Learning to Rank with Top-$k$ Fairness Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang

TMLR 2025 Multi-Output Distributional Fairness via Post-Processing Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang

NeurIPS 2025 Stochastic Momentum Methods for Non-Smooth Non-Convex Finite-Sum Coupled Compositional Optimization Xingyu Chen, Bokun Wang, Ming Yang, Qihang Lin, Tianbao Yang

NeurIPSW 2024 Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang

NeurIPS 2023 Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization Yankun Huang, Qihang Lin

AISTATS 2023 Stochastic Methods for AUC Optimization Subject to AUC-Based Fairness Constraints Yao Yao, Qihang Lin, Tianbao Yang

NeurIPS 2022 Large-Scale Optimization of Partial AUC in a Range of False Positive Rates Yao Yao, Qihang Lin, Tianbao Yang

NeurIPS 2022 ProtoX: Explaining a Reinforcement Learning Agent via Prototyping Ronilo Ragodos, Tong Wang, Qihang Lin, Xun Zhou

JMLR 2021 First-Order Convergence Theory for Weakly-Convex-Weakly-Concave Min-Max Problems Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

JMLR 2021 Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-Box Model Tong Wang, Qihang Lin

JMLR 2020 A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang

IJCAI 2020 Bayesian Decision Process for Budget-Efficient Crowdsourced Clustering Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu

MLJ 2020 High-Dimensional Model Recovery from Random Sketched Data by Exploring Intrinsic Sparsity Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin

NeurIPS 2020 Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

ICML 2020 Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints Runchao Ma, Qihang Lin, Tianbao Yang

ICML 2020 Transparency Promotion with Model-Agnostic Linear Competitors Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani

JMLR 2019 DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen

ICML 2019 Stochastic Optimization for DC Functions and Non-Smooth Non-Convex Regularizers with Non-Asymptotic Convergence Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang

IJCAI 2018 A Unified Analysis of Stochastic Momentum Methods for Deep Learning Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang

ICML 2018 Level-Set Methods for Finite-Sum Constrained Convex Optimization Qihang Lin, Runchao Ma, Tianbao Yang

JMLR 2018 RSG: Beating Subgradient Method Without Smoothness and Strong Convexity Tianbao Yang, Qihang Lin

ICML 2017 A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates Tianbao Yang, Qihang Lin, Lijun Zhang

NeurIPS 2017 ADMM Without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

NeurIPS 2017 Adaptive SVRG Methods Under Error Bound Conditions with Unknown Growth Parameter Yi Xu, Qihang Lin, Tianbao Yang

JMLR 2017 Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

ICML 2017 Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence Yi Xu, Qihang Lin, Tianbao Yang

JMLR 2016 Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing Xi Chen, Kevin Jiao, Qihang Lin

NeurIPS 2016 Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$ Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

MLJ 2016 On Data Preconditioning for Regularized Loss Minimization Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin

UAI 2016 Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang

JMLR 2015 Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling Xi Chen, Qihang Lin, Dengyong Zhou

NeurIPS 2014 An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao

ICML 2014 An Adaptive Accelerated Proximal Gradient Method and Its Homotopy Continuation for Sparse Optimization Qihang Lin, Lin Xiao

ICML 2013 Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing Xi Chen, Qihang Lin, Dengyong Zhou

NeurIPS 2012 Optimal Regularized Dual Averaging Methods for Stochastic Optimization Xi Chen, Qihang Lin, Javier Pena

UAI 2011 Smoothing Proximal Gradient Method for General Structured Sparse Learning Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing