Haihao Lu

According to our database, Haihao Lu authored at least 37 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

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

David L. Applegate, Mateo Díaz, Haihao Lu, Miles Lubin

SIAM J. Optim., March, 2024

A J-symmetric quasi-newton method for minimax problems.

Azam Asl, Haihao Lu, Jinwen Yang

Math. Program., March, 2024

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

Saeed Hajizadeh, Haihao Lu, Benjamin Grimmer

INFORMS J. Optim., January, 2024

Auto-bidding and Auctions in Online Advertising: A Survey.

Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andrés Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo

CoRR, 2024

A Field Guide for Pacing Budget and ROS Constraints.

Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems.

Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni

Oper. Res., January, 2023

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

Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Math. Program., 2023

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

David L. Applegate, Oliver Hinder, Haihao Lu, Miles Lubin

Math. Program., 2023

Joint Feedback Loop for Spend and Return-On-Spend Constraints.

Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

CoRR, 2023

Online Ad Procurement in Non-stationary Autobidding Worlds.

Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou

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.

Haoyue Wang, Haihao Lu, Rahul Mazumder

SIAM J. Optim., December, 2022

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

Haihao Lu

Math. Program., 2022

From Online Optimization to PID Controllers: Mirror Descent with Momentum.

Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni, Balasubramanian Sivan

CoRR, 2022

A J-Symmetric Quasi-Newton Method for Minimax Problems.

Azam Asl, Haihao Lu

CoRR, 2022

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

Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni

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.

Haihao Lu, Robert M. Freund

Math. Program., 2021

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

Haihao Lu, Jinwen Yang

CoRR, 2021

Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient.

David L. Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy

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

Regularized Online Allocation Problems: Fairness and Beyond.

Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni

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

2020

Randomized Gradient Boosting Machine.

Haihao Lu, Rahul Mazumder

SIAM J. Optim., 2020

The Landscape of Nonconvex-Nonconcave Minimax Optimization.

Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni

CoRR, 2020

Contextual Reserve Price Optimization in Auctions.

Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni

CoRR, 2020

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

Haihao Lu

CoRR, 2020

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming.

Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni

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

Dual Mirror Descent for Online Allocation Problems.

Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni

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

Accelerating Gradient Boosting Machines.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

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

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

Kenji Kawaguchi, Haihao Lu

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.

Haihao Lu

INFORMS J. Optim., October, 2019

A Stochastic First-Order Method for Ordered Empirical Risk Minimization.

Kenji Kawaguchi, Haihao Lu

CoRR, 2019

Accelerating Gradient Boosting Machine.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

CoRR, 2019

2018

Relatively Smooth Convex Optimization by First-Order Methods, and Applications.

Haihao Lu, Robert M. Freund, Yurii E. Nesterov

SIAM J. Optim., 2018

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

Robert M. Freund, Haihao Lu

Math. Program., 2018

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

Jonathan Amar, Nicholas Renegar, Haihao Lu

CoRR, 2018

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

Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab S. Mirrokni

CoRR, 2018

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions.

Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni

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

Accelerating Greedy Coordinate Descent Methods.

Haihao Lu, Robert M. Freund, Vahab S. Mirrokni

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

2017

Depth Creates No Bad Local Minima.

Haihao Lu, Kenji Kawaguchi

CoRR, 2017