Learning Preferences with Millions of Parameters by Enforcing Sparsity. - Xi Chen 010; Bing Bai; Yanjun Qi; Qihang Lin; Jaime G. Carbonell (ICDM, 2010)

Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso - Xi Chen 010; Seyoung Kim; Qihang Lin; Jaime G. Carbonell; Eric P. Xing (CoRR, 2010)

An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity - Xi Chen 010; Qihang Lin; Seyoung Kim; Javier Peña 001; Jaime G. Carbonell; Eric P. Xing (CoRR, 2010)

A Smoothing Stochastic Gradient Method for Composite Optimization - Qihang Lin (CoRR, 2010)

Sparse Latent Semantic Analysis. - Xi Chen 010; Yanjun Qi; Bing Bai; Qihang Lin; Jaime G. Carbonell (SDM, 2011)

Smoothing Proximal Gradient Method for General Structured Sparse Learning. - Xi Chen 010; Qihang Lin; Seyoung Kim; Jaime G. Carbonell; Eric P. Xing (UAI, 2011)

Optimal Regularized Dual Averaging Methods for Stochastic Optimization. - Xi Chen 010; Qihang Lin; Javier Peña 001 (NIPS, 2012)

Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing. - Xi Chen; Qihang Lin; Dengyong Zhou (ICML (3), 2013)

An Accelerated Proximal Coordinate Gradient Method. - Qihang Lin; Zhaosong Lu; Lin Xiao (NIPS, 2014)

A sparsity preserving stochastic gradient methods for sparse regression. - Qihang Lin; Xi Chen 010; Javier Peña 001 (Comput. Optim. Appl., 2014)

A smoothing stochastic gradient method for composite optimization. - Qihang Lin; Xi Chen 010; Javier Peña 001 (Optim. Methods Softw., 2014)

Big Data Analytics: Optimization and Randomization. - Tianbao Yang; Qihang Lin; Rong Jin 001 (KDD, 2015)

An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization. - Qihang Lin; Lin Xiao (Comput. Optim. Appl., 2015)

Distributed Stochastic Variance Reduced Gradient Methods. - Jason D. Lee; Tengyu Ma 001; Qihang Lin (CoRR, 2015)

Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. - Tianbao Yang; Lijun Zhang 005; Qihang Lin; Rong Jin 001 (CoRR, 2015)

Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. - Adams Wei Yu; Qihang Lin; Tianbao Yang (CoRR, 2015)

Statistical decision making for optimal budget allocation in crowd labeling. - Xi Chen; Qihang Lin; Dengyong Zhou (J. Mach. Learn. Res., 2015)

A trade execution model under a composite dynamic coherent risk measure. - Qihang Lin; Xi Chen 010; Javier Peña 001 (Oper. Res. Lett., 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)

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/ epsilon). - Yi Xu 008; Yan Yan 006; Qihang Lin; Tianbao Yang (NIPS, 2016)

Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. - Jianhui Chen; Tianbao Yang; Qihang Lin; Lijun Zhang 005; Yi Chang 001 (UAI, 2016)

Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. - Yi Xu 008; Qihang Lin; Tianbao Yang (CoRR, 2016)

Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing. - Xi Chen; Kevin Jiao; Qihang Lin (J. Mach. Learn. Res., 2016)

On Data Preconditioning for Regularized Loss Minimization. - Tianbao Yang; Rong Jin 001; Shenghuo Zhu; Qihang Lin (Mach. Learn., 2016)

A Budget-Constrained Inverse Classification Framework for Smooth Classifiers. - Michael T. Lash; Qihang Lin; W. Nick Street; Jennifer G. Robinson (ICDM Workshops, 2017)

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. - Yi Xu 008; Qihang Lin; Tianbao Yang (ICML, 2017)

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. - Tianbao Yang; Qihang Lin; Lijun Zhang 005 (ICML, 2017)

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. - Yi Xu 008; Mingrui Liu; Qihang Lin; Tianbao Yang (NIPS, 2017)

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. - Yi Xu 008; Qihang Lin; Tianbao Yang (NIPS, 2017)

Generalized Inverse Classification. - Michael T. Lash; Qihang Lin; W. Nick Street; Jennifer G. Robinson; Jeffrey W. Ohlmann (SDM, 2017)

Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training. - Adams Wei Yu; Qihang Lin; Ruslan Salakhutdinov; Jaime G. Carbonell (CoRR, 2017)

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. - Jason D. Lee; Qihang Lin; Tengyu Ma 001; Tianbao Yang (J. Mach. Learn. Res., 2017)

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

A Unified Analysis of Stochastic Momentum Methods for Deep Learning. - Yan Yan 006; Tianbao Yang; Zhe Li 008; Qihang Lin; Yi Yang 001 (IJCAI, 2018)

Prophit: Causal inverse classification for multiple continuously valued treatment policies. - Michael T. Lash; Qihang Lin; W. Nick Street (CoRR, 2018)

Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. - Hassan Rafique; Mingrui Liu; Qihang Lin; Tianbao Yang (CoRR, 2018)

RSG: Beating Subgradient Method without Smoothness and Strong Convexity. - Tianbao Yang; Qihang Lin (J. Mach. Learn. Res., 2018)

A Level-Set Method for Convex Optimization with a Feasible Solution Path. - Qihang Lin; Selvaprabu Nadarajah; Negar Soheili (SIAM J. Optim., 2018)

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. - Yi Xu 008; Qi Qi 006; Qihang Lin; Rong Jin 001; Tianbao Yang (ICML, 2019)

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. - Yan Yan 006; Yi Xu 008; Qihang Lin; Lijun Zhang 005; Tianbao Yang (CoRR, 2019)

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. - Tong Wang 011; 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)

Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models. - Hassan Rafique; Tong Wang 011; Qihang Lin (CoRR, 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)

A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers. - Qihang Lin; Mohit N. Shivdasani; David Tsai 001; Yao-Chuan Chang; Naveen Jayaprakash; Stavros Zanos; Nigel H. Lovell; Socrates Dokos; Tianruo Guo (EMBC, 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 011; Qihang Lin; Arshia Singhani (ICML, 2020)

Bayesian Decision Process for Budget-efficient Crowdsourced Clustering. - Xiaozhou Wang; Xi Chen; Qihang Lin; Weidong Liu (IJCAI, 2020)

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. - Yan Yan 006; Yi Xu 008; Qihang Lin; Wei Liu 005; Tianbao Yang (NeurIPS, 2020)

Self-guided Approximate Linear Programs. - Parshan Pakiman; Selvaprabu Nadarajah; Negar Soheili; Qihang Lin (CoRR, 2020)

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. - Yan Yan 006; Yi Xu 008; Qihang Lin; Wei Liu 005; Tianbao Yang (CoRR, 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)

Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage. - Qihang Lin; Selvaprabu Nadarajah; Negar Soheili (Manag. Sci., 2020)

High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. - Tianbao Yang; Lijun Zhang 005; Qihang Lin; Shenghuo Zhu; Rong Jin 001 (Mach. Learn., 2020)

Comparison-Based Algorithms for One-Dimensional Stochastic Convex Optimization. - Xi Chen; Qihang Lin; Zizhuo Wang 001 (INFORMS J. Optim., 2020)

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)

Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. - Tong Wang 011; Qihang Lin (J. Mach. Learn. Res., 2021)

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. - Ronilo J. Ragodos; Tong Wang 011; Qihang Lin; Xun Zhou 001 (NeurIPS, 2022)

Large-scale Optimization of Partial AUC in a Range of False Positive Rates. - Yao Yao; Qihang Lin; Tianbao Yang (NeurIPS, 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)

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)

Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. - Yankun Huang; Qihang Lin; W. Nick Street; Stephen Baek (CoRR, 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)

Distributionally Robust Optimization with Confidence Bands for Probability Density Functions. - Xi Chen; Qihang Lin; Guanglin Xu (INFORMS J. Optim., 2022)

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. - Yao Yao; Qihang Lin; Tianbao Yang (AISTATS, 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 C. Payne; Mohit N. Shivdasani; David Tsai 001; Nigel H. Lovell; Socrates Dokos; Tianruo Guo (EMBC, 2023)

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

Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints. - Yankun Huang; Qihang Lin (CoRR, 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)

Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method. - Qihang Lin; Yangyang Xu (SIAM J. Optim., 2023)

FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning. - Brianna Mueller; W. Nick Street; Stephen Baek; Qihang Lin; Jingyi Yang; Yankun Huang (IEEE Big Data, 2024)

Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. - Qi Qi 006; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 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)

Self-Guided Approximate Linear Programs: Randomized Multi-Shot Approximation of Discounted Cost Markov Decision Processes. - Parshan Pakiman; Selvaprabu Nadarajah; Negar Soheili; Qihang Lin (Manag. Sci., 2025)

Multi-Output Distributional Fairness via Post-Processing. - Gang Li; Qihang Lin; Ayush Ghosh; Tianbao Yang (Trans. Mach. Learn. Res., 2025)

Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions. - Yankun Huang; Qihang Lin; Yangyang Xu (CoRR, 2025)

Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints. - Ming Yang; Gang Li; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 2025)

Enforcing Fairness Where It Matters: An Approach Based on Difference-of-Convex Constraints. - Yutian He; Yankun Huang; Yao Yao; Qihang Lin (CoRR, 2025)

Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization. - Xingyu Chen; Bokun Wang; Min Yang 007; Quanqi Hu; Qihang Lin; Tianbao Yang (CoRR, 2025)