Qihang Lin

According to our database, Qihang Lin authored at least 73 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 2024

Multi-Output Distributional Fairness via Post-Processing.

Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang

CoRR, 2024

Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning.

Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang

CoRR, 2024

2023

Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method.

Qihang Lin, Yangyang Xu

SIAM J. Optim., March, 2023

First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods.

Wei Liu, Qihang Lin, Yangyang Xu

CoRR, 2023

Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints.

Yankun Huang, Qihang Lin

CoRR, 2023

Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization.

Yankun Huang, Qihang Lin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Modulating functionally-distinct vagus nerve fibers using microelectrodes and kilohertz frequency electrical stimulation.

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

Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints.

Yao Yao, Qihang Lin, Tianbao Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Distributionally Robust Optimization with Confidence Bands for Probability Density Functions.

Xi Chen, Qihang Lin, Guanglin Xu

INFORMS J. Optim., January, 2022

Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning.

Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang

Optim. Methods Softw., 2022

Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization.

Yankun Huang, Qihang Lin, W. Nick Street, Stephen Baek

CoRR, 2022

Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems.

Qihang Lin, Yangyang Xu

CoRR, 2022

Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization.

Qihang Lin, Runchao Ma, Yangyang Xu

Comput. Optim. Appl., 2022

Large-scale Optimization of Partial AUC in a Range of False Positive Rates.

Yao Yao, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping.

Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model.

Tong Wang, Qihang Lin

J. Mach. Learn. Res., 2021

First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems.

Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang

J. Mach. Learn. Res., 2021

2020

Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization.

Xi Chen, Qihang Lin, Zizhuo Wang

INFORMS J. Optim., January, 2020

High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.

Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin

Mach. Learn., 2020

Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage.

Qihang Lin, Selvaprabu Nadarajah, Negar Soheili

Manag. Sci., 2020

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.

Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang

J. Mach. Learn. Res., 2020

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

CoRR, 2020

Self-guided Approximate Linear Programs.

Parshan Pakiman, Selvaprabu Nadarajah, Negar Soheili, Qihang Lin

CoRR, 2020

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bayesian Decision Process for Budget-efficient Crowdsourced Clustering.

Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Transparency Promotion with Model-Agnostic Linear Competitors.

Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani

Proceedings of the 37th International Conference on Machine Learning, 2020

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints.

Runchao Ma, Qihang Lin, Tianbao Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers.

Qihang Lin, Mohit N. Shivdasani, David Tsai, Yao-Chuan Chang, Naveen Jayaprakash, Stavros Zanos, Nigel H. Lovell, Socrates Dokos, Tianruo Guo

Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019

DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization.

Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen

J. Mach. Learn. Res., 2019

Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models.

Hassan Rafique, Tong Wang, Qihang Lin

CoRR, 2019

Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints.

Qihang Lin, Runchao Ma, Yangyang Xu

CoRR, 2019

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model.

Tong Wang, Qihang Lin

CoRR, 2019

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure.

Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, Tianbao Yang

CoRR, 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

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

A Level-Set Method for Convex Optimization with a Feasible Solution Path.

Qihang Lin, Selvaprabu Nadarajah, Negar Soheili

SIAM J. Optim., 2018

RSG: Beating Subgradient Method without Smoothness and Strong Convexity.

Tianbao Yang, Qihang Lin

J. Mach. Learn. Res., 2018

Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.

Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang

CoRR, 2018

Prophit: Causal inverse classification for multiple continuously valued treatment policies.

Michael T. Lash, Qihang Lin, W. Nick Street

CoRR, 2018

A Unified Analysis of Stochastic Momentum Methods for Deep Learning.

Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Level-Set Methods for Finite-Sum Constrained Convex Optimization.

Qihang Lin, Runchao Ma, Tianbao Yang

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.

Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang

J. Mach. Learn. Res., 2017

Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training.

Adams Wei Yu, Qihang Lin, Ruslan Salakhutdinov, Jaime G. Carbonell

CoRR, 2017

Generalized Inverse Classification.

Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson, Jeffrey W. Ohlmann

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization.

Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates.

Tianbao Yang, Qihang Lin, Lijun Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence.

Yi Xu, Qihang Lin, Tianbao Yang

Proceedings of the 34th International Conference on Machine Learning, 2017

A Budget-Constrained Inverse Classification Framework for Smooth Classifiers.

Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson

Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017

2016

On Data Preconditioning for Regularized Loss Minimization.

Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin

Mach. Learn., 2016

Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing.

Xi Chen, Kevin Jiao, Qihang Lin

J. Mach. Learn. Res., 2016

Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound.

Yi Xu, Qihang Lin, Tianbao Yang

CoRR, 2016

Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections.

Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon).

Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization.

Qihang Lin, Zhaosong Lu, Lin Xiao

SIAM J. Optim., 2015

A trade execution model under a composite dynamic coherent risk measure.

Qihang Lin, Xi Chen, Javier Peña

Oper. Res. Lett., 2015

Statistical decision making for optimal budget allocation in crowd labeling.

Xi Chen, Qihang Lin, Dengyong Zhou

J. Mach. Learn. Res., 2015

Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data.

Adams Wei Yu, Qihang Lin, Tianbao Yang

CoRR, 2015

Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees.

Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin

CoRR, 2015

Distributed Stochastic Variance Reduced Gradient Methods.

Jason D. Lee, Tengyu Ma, Qihang Lin

CoRR, 2015

An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization.

Qihang Lin, Lin Xiao

Comput. Optim. Appl., 2015

Big Data Analytics: Optimization and Randomization.

Tianbao Yang, Qihang Lin, Rong Jin

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

A smoothing stochastic gradient method for composite optimization.

Qihang Lin, Xi Chen, Javier Peña

Optim. Methods Softw., 2014

A sparsity preserving stochastic gradient methods for sparse regression.

Qihang Lin, Xi Chen, Javier Peña

Comput. Optim. Appl., 2014

An Accelerated Proximal Coordinate Gradient Method.

Qihang Lin, Zhaosong Lu, Lin Xiao

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing.

Xi Chen, Qihang Lin, Dengyong Zhou

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Optimal Regularized Dual Averaging Methods for Stochastic Optimization.

Xi Chen, Qihang Lin, Javier Peña

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Smoothing Proximal Gradient Method for General Structured Sparse Learning.

Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

Proceedings of the UAI 2011, 2011

Sparse Latent Semantic Analysis.

Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell

Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

2010

A Smoothing Stochastic Gradient Method for Composite Optimization

Qihang Lin

CoRR, 2010

An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity

Xi Chen, Qihang Lin, Seyoung Kim, Javier Peña, Jaime G. Carbonell, Eric P. Xing

CoRR, 2010

Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing

CoRR, 2010

Learning Preferences with Millions of Parameters by Enforcing Sparsity.

Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G. Carbonell

Proceedings of the ICDM 2010, 2010