Qihang Lin - RankMe

97 papers A* 15A 5B 1Journal 67Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Single-loop Algorithms for Stochastic Non-Convex Optimization with Weakly-Convex Constraints. Trans. Mach. Learn. Res. Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang
2025	J	`jnl`	An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition. J. Mach. Learn. Res. Qihang Lin, Negar Soheili, Runchao Ma, Selvaprabu Nadarajah
2025	J	`jnl`	Enforcing Fairness Where It Matters: An Approach Based on Difference-of-Convex Constraints. CoRR Yutian He, Yankun Huang, Yao Yao, Qihang Lin
2025	J	`jnl`	Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions. CoRR Yankun Huang, Qihang Lin, Yangyang Xu
2025	J	`jnl`	Learning to Rank with Top- CoRR Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Learning to Rank with Top-$K$ Fairness. Trans. Mach. Learn. Res. Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Multi-Output Distributional Fairness via Post-Processing. Trans. Mach. Learn. Res. Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang
2025	J	`jnl`	Self-Guided Approximate Linear Programs: Randomized Multi-Shot Approximation of Discounted Cost Markov Decision Processes. Manag. Sci. Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin
2025	J	`jnl`	Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints. CoRR Ming Yang, Gang Li, Quanqi Hu, Qihang Lin, Tianbao Yang
2025	J	`jnl`	Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization. CoRR Xingyu Chen, Bokun Wang, Min Yang, Quanqi Hu, Qihang Lin, Tianbao Yang
2024	B	`conf`	FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning. IEEE Big Data Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin, Jingyi Yang, Yankun Huang
2024	J	`jnl`	FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning. CoRR Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin, Jingyi Yang, Yankun Huang
2024	J	`jnl`	Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models. CoRR Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang
2024	J	`jnl`	Multi-Output Distributional Fairness via Post-Processing. CoRR Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang
2024	J	`jnl`	Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang
2023	J	`jnl`	First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods. CoRR Wei Liu, Qihang Lin, Yangyang Xu
2023	—	`conf`	Modulating functionally-distinct vagus nerve fibers using microelectrodes and kilohertz frequency electrical stimulation. EMBC Peijun Qin, Qihang Lin, Yuyang Xie, Yao-Chuan Chang, Stavros Zanos, Hao Wang, Sophie C. Payne, Mohit N. Shivdasani, David Tsai, Nigel H. Lovell, Socrates Dokos, Tianruo Guo
2023	A*	`conf`	Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization. NeurIPS Yankun Huang, Qihang Lin
2023	J	`jnl`	Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method. SIAM J. Optim. Qihang Lin, Yangyang Xu
2023	J	`jnl`	Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints. CoRR Yankun Huang, Qihang Lin
2023	A	`conf`	Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS Yao Yao, Qihang Lin, Tianbao Yang
2022	J	`jnl`	Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization. Comput. Optim. Appl. Qihang Lin, Runchao Ma, Yangyang Xu
2022	J	`jnl`	Distributionally Robust Optimization with Confidence Bands for Probability Density Functions. INFORMS J. Optim. Xi Chen, Qihang Lin, Guanglin Xu
2022	J	`jnl`	Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. CoRR Yankun Huang, Qihang Lin, W. Nick Street, Stephen Baek
2022	J	`jnl`	Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems. CoRR Qihang Lin, Yangyang Xu
2022	A*	`conf`	Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS Yao Yao, Qihang Lin, Tianbao Yang
2022	J	`jnl`	Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR Yao Yao, Qihang Lin, Tianbao Yang
2022	A*	`conf`	ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. NeurIPS Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou
2022	J	`jnl`	ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. CoRR Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou
2022	J	`jnl`	Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR Yao Yao, Qihang Lin, Tianbao Yang
2022	J	`jnl`	Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2021	J	`jnl`	First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang
2021	J	`jnl`	Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. J. Mach. Learn. Res. Tong Wang, Qihang Lin
2020	—	`conf`	A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers. EMBC Qihang Lin, Mohit N. Shivdasani, David Tsai, Yao-Chuan Chang, Naveen Jayaprakash, Stavros Zanos, Nigel H. Lovell, Socrates Dokos, Tianruo Guo
2020	J	`jnl`	A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. J. Mach. Learn. Res. Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
2020	A*	`conf`	Bayesian Decision Process for Budget-efficient Crowdsourced Clustering. IJCAI Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu
2020	J	`jnl`	Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization. INFORMS J. Optim. Xi Chen, Qihang Lin, Zizhuo Wang
2020	J	`jnl`	High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. Mach. Learn. Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin
2020	A*	`conf`	Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	A*	`conf`	Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML Runchao Ma, Qihang Lin, Tianbao Yang
2020	J	`jnl`	Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage. Manag. Sci. Qihang Lin, Selvaprabu Nadarajah, Negar Soheili
2020	J	`jnl`	Self-guided Approximate Linear Programs. CoRR Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin
2020	J	`jnl`	Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
2020	A*	`conf`	Transparency Promotion with Model-Agnostic Linear Competitors. ICML Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani
2019	J	`jnl`	A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
2019	J	`jnl`	DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization. J. Mach. Learn. Res. Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen
2019	J	`jnl`	Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. CoRR Tong Wang, Qihang Lin
2019	J	`jnl`	Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints. CoRR Qihang Lin, Runchao Ma, Yangyang Xu
2019	J	`jnl`	Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models. CoRR Hassan Rafique, Tong Wang, Qihang Lin
2019	A*	`conf`	Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang
2019	J	`jnl`	Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang
2018	J	`jnl`	A Level-Set Method for Convex Optimization with a Feasible Solution Path. SIAM J. Optim. Qihang Lin, Selvaprabu Nadarajah, Negar Soheili
2018	A*	`conf`	A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
2018	J	`jnl`	A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
2018	A*	`conf`	Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML Qihang Lin, Runchao Ma, Tianbao Yang
2018	J	`jnl`	Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang
2018	J	`jnl`	Prophit: Causal inverse classification for multiple continuously valued treatment policies. CoRR Michael T. Lash, Qihang Lin, W. Nick Street
2018	J	`jnl`	RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. Tianbao Yang, Qihang Lin
2017	—	`conf`	A Budget-Constrained Inverse Classification Framework for Smooth Classifiers. ICDM Workshops Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
2017	A*	`conf`	A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML Tianbao Yang, Qihang Lin, Lijun Zhang
2017	—	`conf`	ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
2017	—	`conf`	Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS Yi Xu, Qihang Lin, Tianbao Yang
2017	J	`jnl`	Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. J. Mach. Learn. Res. Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
2017	A	`conf`	Generalized Inverse Classification. SDM Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann
2017	J	`jnl`	Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. CoRR Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell
2017	A*	`conf`	Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML Yi Xu, Qihang Lin, Tianbao Yang
2016	J	`jnl`	A budget-constrained inverse classification framework for smooth classifiers. CoRR Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
2016	J	`jnl`	Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR Yi Xu, Qihang Lin, Tianbao Yang
2016	J	`jnl`	Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. CoRR Xi Chen, Kevin Jiao, Qihang Lin
2016	J	`jnl`	Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. J. Mach. Learn. Res. Xi Chen, Kevin Jiao, Qihang Lin
2016	J	`jnl`	Generalized Inverse Classification. CoRR Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann
2016	—	`conf`	Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
2016	J	`jnl`	On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
2016	A	`conf`	Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang
2015	J	`jnl`	A trade execution model under a composite dynamic coherent risk measure. Oper. Res. Lett. Qihang Lin, Xi Chen, Javier Peña
2015	J	`jnl`	An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization. SIAM J. Optim. Qihang Lin, Zhaosong Lu, Lin Xiao
2015	J	`jnl`	An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization. Comput. Optim. Appl. Qihang Lin, Lin Xiao
2015	A*	`conf`	Big Data Analytics: Optimization and Randomization. KDD Tianbao Yang, Qihang Lin, Rong Jin
2015	J	`jnl`	Distributed Stochastic Variance Reduced Gradient Methods. CoRR Jason D. Lee, Tengyu Ma, Qihang Lin
2015	J	`jnl`	Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR Adams Wei Yu, Qihang Lin, Tianbao Yang
2015	J	`jnl`	Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin
2015	J	`jnl`	Statistical decision making for optimal budget allocation in crowd labeling. J. Mach. Learn. Res. Xi Chen, Qihang Lin, Dengyong Zhou
2014	J	`jnl`	A smoothing stochastic gradient method for composite optimization. Optim. Methods Softw. Qihang Lin, Xi Chen, Javier Peña
2014	J	`jnl`	A sparsity preserving stochastic gradient methods for sparse regression. Comput. Optim. Appl. Qihang Lin, Xi Chen, Javier Peña
2014	—	`conf`	An Accelerated Proximal Coordinate Gradient Method. NIPS Qihang Lin, Zhaosong Lu, Lin Xiao
2014	A*	`conf`	An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization. ICML Qihang Lin, Lin Xiao
2014	J	`jnl`	On Data Preconditioning for Regularized Loss Minimization. CoRR Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
2014	J	`jnl`	Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. CoRR Xi Chen, Qihang Lin, Dengyong Zhou
2013	—	`conf`	Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. ICML (3) Xi Chen, Qihang Lin, Dengyong Zhou
2012	—	`conf`	Optimal Regularized Dual Averaging Methods for Stochastic Optimization. NIPS Xi Chen, Qihang Lin, Javier Peña
2012	J	`jnl`	Smoothing Proximal Gradient Method for General Structured Sparse Learning CoRR Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing
2011	A	`conf`	Smoothing Proximal Gradient Method for General Structured Sparse Learning. UAI Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing
2011	A	`conf`	Sparse Latent Semantic Analysis. SDM Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell
2010	J	`jnl`	A Smoothing Stochastic Gradient Method for Composite Optimization CoRR Qihang Lin
2010	J	`jnl`	An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity CoRR Xi Chen, Qihang Lin, Seyoung Kim, Javier Peña, Jaime G. Carbonell, Eric P. Xing
2010	J	`jnl`	Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso CoRR Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing
2010	A*	`conf`	Learning Preferences with Millions of Parameters by Enforcing Sparsity. ICDM Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G. Carbonell