# Haihao Lu

Orcid: 0000-0002-5217-1894
According to our database

Collaborative distances:

^{1}, Haihao Lu authored at least 35 papers between 2017 and 2024.Collaborative distances:

## Timeline

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#### On csauthors.net:

## Bibliography

2024

Infeasibility Detection with Primal-Dual Hybrid Gradient for Large-Scale Linear Programming.

SIAM J. Optim., March, 2024

Math. Program., March, 2024

On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems.

INFORMS J. Optim., January, 2024

2023

Oper. Res., January, 2023

The landscape of the proximal point method for nonconvex-nonconcave minimax optimization.

Math. Program., 2023

Faster first-order primal-dual methods for linear programming using restarts and sharpness.

Math. Program., 2023

CoRR, 2023

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

2022

Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning.

SIAM J. Optim., December, 2022

An O(s<sup>r)</sup>-resolution ODE framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems.

Math. Program., 2022

CoRR, 2022

CoRR, 2022

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems.

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021

Generalized stochastic Frank-Wolfe algorithm with stochastic "substitute" gradient for structured convex optimization.

Math. Program., 2021

Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts.

CoRR, 2021

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

SIAM J. Optim., 2020

CoRR, 2020

CoRR, 2020

An O(s<sup>r</sup>)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems.

CoRR, 2020

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

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent.

INFORMS J. Optim., October, 2019

CoRR, 2019

CoRR, 2019

2018

SIAM J. Optim., 2018

New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure.

Math. Program., 2018

Near-Optimal Online Knapsack Strategy for Real-Time Bidding in Internet Advertising.

CoRR, 2018

Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems.

CoRR, 2018

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

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

2017

CoRR, 2017