Qihang Lin
Orcid: 0000-0003-2943-3267
According to our database1,
Qihang Lin
authored at least 71 papers
between 2010 and 2024.
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Bibliography
2024
CoRR, 2024
2023
Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method.
SIAM J. Optim., March, 2023
First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods.
CoRR, 2023
Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints.
CoRR, 2023
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization.
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.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
Distributionally Robust Optimization with Confidence Bands for Probability Density Functions.
INFORMS J. Optim., January, 2022
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning.
Optim. Methods Softw., 2022
Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems.
CoRR, 2022
Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization.
Comput. Optim. Appl., 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
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.
J. Mach. Learn. Res., 2021
J. Mach. Learn. Res., 2021
2020
INFORMS J. Optim., January, 2020
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.
Mach. Learn., 2020
Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage.
Manag. Sci., 2020
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.
J. Mach. Learn. Res., 2020
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints.
Proceedings of the 37th International Conference on Machine Learning, 2020
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.
J. Mach. Learn. Res., 2019
Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models.
CoRR, 2019
Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints.
CoRR, 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model.
CoRR, 2019
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure.
CoRR, 2019
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence.
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
SIAM J. Optim., 2018
J. Mach. Learn. Res., 2018
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.
CoRR, 2018
Prophit: Causal inverse classification for multiple continuously valued treatment policies.
CoRR, 2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
2017
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.
J. Mach. Learn. Res., 2017
CoRR, 2017
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017
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.
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.
Proceedings of the 34th International Conference on Machine Learning, 2017
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence.
Proceedings of the 34th International Conference on Machine Learning, 2017
Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017
2016
J. Mach. Learn. Res., 2016
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016
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.
SIAM J. Optim., 2015
Oper. Res. Lett., 2015
J. Mach. Learn. Res., 2015
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data.
CoRR, 2015
An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization.
Comput. Optim. Appl., 2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
2014
Optim. Methods Softw., 2014
Comput. Optim. Appl., 2014
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
2013
Proceedings of the 30th International Conference on Machine Learning, 2013
2012
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
Proceedings of the UAI 2011, 2011
Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011
2010
An Efficient Proximal-Gradient Method for Single and Multi-task Regression with Structured Sparsity
CoRR, 2010
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso
CoRR, 2010
Proceedings of the ICDM 2010, 2010