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ORCIDHaihao Lu
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2020 – today
- 2024
[j14]Saeed Hajizadeh
, Haihao Lu, Benjamin Grimmer:
On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems. INFORMS J. Optim. 6(1): 19-31 (2024)- [j13]Azam Asl, Haihao Lu, Jinwen Yang:
A J-symmetric quasi-newton method for minimax problems. Math. Program. 204(1): 207-254 (2024) - [j12]David L. Applegate, Mateo Díaz, Haihao Lu, Miles Lubin:
Infeasibility Detection with Primal-Dual Hybrid Gradient for Large-Scale Linear Programming. SIAM J. Optim. 34(1): 459-484 (2024) - [j11]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:
Auto-Bidding and Auctions in Online Advertising: A Survey. SIGecom Exch. 22(1): 159-183 (2024) - [c11]Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
A Field Guide for Pacing Budget and ROS Constraints. ICML 2024 - [i20]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:
Auto-bidding and Auctions in Online Advertising: A Survey. CoRR abs/2408.07685 (2024) - [i19]Haihao Lu, Zedong Peng, Jinwen Yang:
MPAX: Mathematical Programming in JAX. CoRR abs/2412.09734 (2024) - 2023
- [j10]Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni:
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. Oper. Res. 71(1): 101-119 (2023) - [j9]David L. Applegate, Oliver Hinder, Haihao Lu, Miles Lubin:
Faster first-order primal-dual methods for linear programming using restarts and sharpness. Math. Program. 201(1): 133-184 (2023) - [j8]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni:
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. Math. Program. 201(1): 373-407 (2023) - [c10]Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou:
Online Ad Procurement in Non-stationary Autobidding Worlds. NeurIPS 2023 - [i18]Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Joint Feedback Loop for Spend and Return-On-Spend Constraints. CoRR abs/2302.08530 (2023) - [i17]Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou:
Online Ad Procurement in Non-stationary Autobidding Worlds. CoRR abs/2307.05698 (2023) - 2022
- [j7]Haihao Lu:
An O(sr)-resolution ODE framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems. Math. Program. 194(1): 1061-1112 (2022) - [j6]Haoyue Wang, Haihao Lu, Rahul Mazumder:
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. SIAM J. Optim. 32(4): 2938-2968 (2022) - [c9]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni:
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. ALT 2022: 465-487 - [i16]Azam Asl, Haihao Lu:
A J-Symmetric Quasi-Newton Method for Minimax Problems. CoRR abs/2202.02279 (2022) - [i15]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni, Balasubramanian Sivan:
From Online Optimization to PID Controllers: Mirror Descent with Momentum. CoRR abs/2202.06152 (2022) - 2021
- [j5]Haihao Lu, Robert M. Freund:
Generalized stochastic Frank-Wolfe algorithm with stochastic "substitute" gradient for structured convex optimization. Math. Program. 187(1): 317-349 (2021) - [c8]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Regularized Online Allocation Problems: Fairness and Beyond. ICML 2021: 630-639 - [c7]David L. Applegate, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, Warren Schudy:
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient. NeurIPS 2021: 20243-20257 - [i14]Haihao Lu, Jinwen Yang:
Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts. CoRR abs/2111.05530 (2021) - 2020
- [j4]Haihao Lu, Rahul Mazumder:
Randomized Gradient Boosting Machine. SIAM J. Optim. 30(4): 2780-2808 (2020) - [c6]Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machines. AISTATS 2020: 516-526 - [c5]Kenji Kawaguchi, Haihao Lu:
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. AISTATS 2020: 669-679 - [c4]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Dual Mirror Descent for Online Allocation Problems. ICML 2020: 613-628 - [c3]Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni:
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. NeurIPS 2020 - [i13]Haihao Lu:
An O(sr)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems. CoRR abs/2001.08826 (2020) - [i12]Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni:
Contextual Reserve Price Optimization in Auctions. CoRR abs/2002.08841 (2020) - [i11]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni:
The Landscape of Nonconvex-Nonconcave Minimax Optimization. CoRR abs/2006.08667 (2020) - [i10]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Regularized Online Allocation Problems: Fairness and Beyond. CoRR abs/2007.00514 (2020) - [i9]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni:
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. CoRR abs/2010.10628 (2020) - [i8]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. CoRR abs/2011.10124 (2020)
2010 – 2019
- 2019
- [j3]Haihao Lu:
"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent. INFORMS J. Optim. 1(4): 288-303 (2019) - [i7]Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machine. CoRR abs/1903.08708 (2019) - [i6]Kenji Kawaguchi, Haihao Lu:
A Stochastic First-Order Method for Ordered Empirical Risk Minimization. CoRR abs/1907.04371 (2019) - 2018
- [j2]Robert M. Freund, Haihao Lu:
New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure. Math. Program. 170(2): 445-477 (2018) - [j1]Haihao Lu, Robert M. Freund, Yurii E. Nesterov:
Relatively Smooth Convex Optimization by First-Order Methods, and Applications. SIAM J. Optim. 28(1): 333-354 (2018) - [c2]Haihao Lu, Robert M. Freund, Vahab S. Mirrokni:
Accelerating Greedy Coordinate Descent Methods. ICML 2018: 3263-3272 - [c1]Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni:
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. ICML 2018: 5215-5224 - [i5]Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni:
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. CoRR abs/1807.02694 (2018) - [i4]Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab S. Mirrokni:
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems. CoRR abs/1810.02716 (2018) - [i3]Haihao Lu, Rahul Mazumder:
Randomized Gradient Boosting Machine. CoRR abs/1810.10158 (2018) - [i2]Jonathan Amar, Nicholas Renegar, Haihao Lu:
Near-Optimal Online Knapsack Strategy for Real-Time Bidding in Internet Advertising. CoRR abs/1810.10661 (2018) - 2017
- [i1]Haihao Lu, Kenji Kawaguchi:
Depth Creates No Bad Local Minima. CoRR abs/1702.08580 (2017)
Coauthor Index
aka: Vahab Mirrokni
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