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2020 – today
- 2025
[i35]Yankun Huang, Qihang Lin, Yangyang Xu:
Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions. CoRR abs/2502.19764 (2025)- 2024
- [c30]Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin, Jingyi Yang, Yankun Huang:
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning. IEEE Big Data 2024: 7961-7970 - [i34]Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang:
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR abs/2406.05686 (2024) - [i33]Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang:
Multi-Output Distributional Fairness via Post-Processing. CoRR abs/2409.00553 (2024) - [i32]Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang:
Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models. CoRR abs/2410.03955 (2024) - [i31]Brianna Mueller, W. Nick Street, Stephen Baek, Qihang Lin, Jingyi Yang, Yankun Huang:
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning. CoRR abs/2410.14075 (2024) - 2023
- [j22]Qihang Lin
, Yangyang Xu:
Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method. SIAM J. Optim. 33(1): 1-35 (2023) - [c29]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS 2023: 10324-10342 - [c28]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:
Modulating functionally-distinct vagus nerve fibers using microelectrodes and kilohertz frequency electrical stimulation. EMBC 2023: 1-4 - [c27]Yankun Huang, Qihang Lin:
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization. NeurIPS 2023 - [i30]Yankun Huang, Qihang Lin:
Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints. CoRR abs/2301.13314 (2023) - [i29]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 abs/2307.07605 (2023) - 2022
- [j21]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): 175-224 (2022) - [j20]Xi Chen, Qihang Lin, Guanglin Xu:
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions. INFORMS J. Optim. 4(1): 65-89 (2022) - [j19]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): 1087-1121 (2022) - [c26]Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou:
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. NeurIPS 2022 - [c25]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS 2022 - [i28]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 abs/2201.01169 (2022) - [i27]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR abs/2203.01505 (2022) - [i26]Yankun Huang, Qihang Lin, W. Nick Street, Stephen Baek:
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. CoRR abs/2207.10751 (2022) - [i25]Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou:
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. CoRR abs/2211.03162 (2022) - [i24]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR abs/2212.12603 (2022) - 2021
- [j18]Tong Wang, Qihang Lin:
Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. J. Mach. Learn. Res. 22: 137:1-137:38 (2021) - [j17]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: 169:1-169:34 (2021) - 2020
- [j16]Xi Chen, Qihang Lin, Zizhuo Wang:
Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization. INFORMS J. Optim. 2(1): 34-56 (2020) - [j15]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: 143:1-143:45 (2020) - [j14]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): 1544-1562 (2020) - [j13]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): 899-938 (2020) - [c24]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: 2475-2478 - [c23]Runchao Ma, Qihang Lin, Tianbao Yang:
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML 2020: 6554-6564 - [c22]Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani:
Transparency Promotion with Model-Agnostic Linear Competitors. ICML 2020: 7898-7908 - [c21]Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu:
Bayesian Decision Process for Budget-efficient Crowdsourced Clustering. IJCAI 2020: 2044-2050 - [c20]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020 - [i23]Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin:
Self-guided Approximate Linear Programs. CoRR abs/2001.02798 (2020) - [i22]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR abs/2002.05309 (2020)
2010 – 2019
- 2019
- [j12]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: 43:1-43:58 (2019) - [c19]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: 6942-6951 - [i21]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 abs/1904.10112 (2019) - [i20]Tong Wang, Qihang Lin:
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. CoRR abs/1905.04241 (2019) - [i19]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang:
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR abs/1908.03077 (2019) - [i18]Qihang Lin, Runchao Ma, Yangyang Xu:
Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints. CoRR abs/1908.11518 (2019) - [i17]Hassan Rafique, Tong Wang, Qihang Lin:
Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models. CoRR abs/1909.10467 (2019) - 2018
- [j11]Tianbao Yang, Qihang Lin:
RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. 19: 6:1-6:33 (2018) - [j10]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili:
A Level-Set Method for Convex Optimization with a Feasible Solution Path. SIAM J. Optim. 28(4): 3290-3311 (2018) - [c18]Qihang Lin, Runchao Ma, Tianbao Yang:
Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML 2018: 3118-3127 - [c17]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI 2018: 2955-2961 - [i16]Michael T. Lash, Qihang Lin, W. Nick Street:
Prophit: Causal inverse classification for multiple continuously valued treatment policies. CoRR abs/1802.04918 (2018) - [i15]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR abs/1808.10396 (2018) - [i14]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR abs/1810.02060 (2018) - 2017
- [j9]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: 122:1-122:43 (2017) - [c16]Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson:
A Budget-Constrained Inverse Classification Framework for Smooth Classifiers. ICDM Workshops 2017: 1184-1193 - [c15]Yi Xu, Qihang Lin, Tianbao Yang:
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML 2017: 3821-3830 - [c14]Tianbao Yang, Qihang Lin, Lijun Zhang:
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML 2017: 3901-3910 - [c13]Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS 2017: 1267-1277 - [c12]Yi Xu, Qihang Lin, Tianbao Yang:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS 2017: 3277-3287 - [c11]Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann:
Generalized Inverse Classification. SDM 2017: 162-170 - [i13]Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell:
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. CoRR abs/1707.04822 (2017) - 2016
- [j8]Xi Chen, Kevin Jiao, Qihang Lin:
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. J. Mach. Learn. Res. 17: 217:1-217:40 (2016) - [j7]Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin:
On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. 103(1): 57-79 (2016) - [c10]Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang:
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS 2016: 1208-1216 - [c9]Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang:
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI 2016 - [i12]Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson:
A budget-constrained inverse classification framework for smooth classifiers. CoRR abs/1605.09068 (2016) - [i11]Yi Xu, Qihang Lin, Tianbao Yang:
Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR abs/1607.01027 (2016) - [i10]Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann:
Generalized Inverse Classification. CoRR abs/1610.01675 (2016) - [i9]Xi Chen, Kevin Jiao, Qihang Lin:
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. CoRR abs/1612.07222 (2016) - 2015
- [j6]Qihang Lin, Lin Xiao:
An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization. Comput. Optim. Appl. 60(3): 633-674 (2015) - [j5]Xi Chen, Qihang Lin, Dengyong Zhou:
Statistical decision making for optimal budget allocation in crowd labeling. J. Mach. Learn. Res. 16: 1-46 (2015) - [j4]Qihang Lin, Xi Chen, Javier Peña:
A trade execution model under a composite dynamic coherent risk measure. Oper. Res. Lett. 43(1): 52-58 (2015) - [j3]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): 2244-2273 (2015) - [c8]Tianbao Yang, Qihang Lin, Rong Jin:
Big Data Analytics: Optimization and Randomization. KDD 2015: 2327 - [i8]Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin:
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR abs/1507.05185 (2015) - [i7]Jason D. Lee, Tengyu Ma, Qihang Lin:
Distributed Stochastic Variance Reduced Gradient Methods. CoRR abs/1507.07595 (2015) - [i6]Adams Wei Yu, Qihang Lin, Tianbao Yang:
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR abs/1508.03390 (2015) - 2014
- [j2]Qihang Lin, Xi Chen, Javier Peña:
A sparsity preserving stochastic gradient methods for sparse regression. Comput. Optim. Appl. 58(2): 455-482 (2014) - [j1]Qihang Lin, Xi Chen, Javier Peña:
A smoothing stochastic gradient method for composite optimization. Optim. Methods Softw. 29(6): 1281-1301 (2014) - [c7]Qihang Lin, Lin Xiao:
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization. ICML 2014: 73-81 - [c6]Qihang Lin, Zhaosong Lu, Lin Xiao:
An Accelerated Proximal Coordinate Gradient Method. NIPS 2014: 3059-3067 - [i5]Xi Chen, Qihang Lin, Dengyong Zhou:
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling. CoRR abs/1403.3080 (2014) - 2013
- [c5]Xi Chen, Qihang Lin, Dengyong Zhou:
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. ICML (3) 2013: 64-72 - 2012
- [c4]Xi Chen, Qihang Lin, Javier Peña:
Optimal Regularized Dual Averaging Methods for Stochastic Optimization. NIPS 2012: 404-412 - [i4]Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing:
Smoothing Proximal Gradient Method for General Structured Sparse Learning. CoRR abs/1202.3708 (2012) - 2011
- [c3]Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell:
Sparse Latent Semantic Analysis. SDM 2011: 474-485 - [c2]Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing:
Smoothing Proximal Gradient Method for General Structured Sparse Learning. UAI 2011: 105-114 - 2010
- [c1]Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G. Carbonell:
Learning Preferences with Millions of Parameters by Enforcing Sparsity. ICDM 2010: 779-784 - [i3]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 abs/1005.3579 (2010) - [i2]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 abs/1005.4717 (2010) - [i1]Qihang Lin:
A Smoothing Stochastic Gradient Method for Composite Optimization. CoRR abs/1008.5204 (2010)
Coauthor Index
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