Haihao Lu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 41

Top Topics

Internet Advertising

Gradient Boosting

Convex Optimization

Top Venues

Publications

Saeed Hajizadeh, Haihao Lu, Benjamin Grimmer
On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems. INFORMS J. Optim. 6 (1) (2024)
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) (2024)
Azam Asl, Haihao Lu, Jinwen Yang
A J-symmetric quasi-newton method for minimax problems. Math. Program. 204 (1) (2024)
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 (2023)
Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou
Online Ad Procurement in Non-stationary Autobidding Worlds. CoRR (2023)
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. Math. Program. 201 (1) (2023)
Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. Oper. Res. 71 (1) (2023)
Jason Cheuk Nam Liang, Haihao Lu, Baoyu Zhou
Online Ad Procurement in Non-stationary Autobidding Worlds. NeurIPS (2023)
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) (2023)
Azam Asl, Haihao Lu
A J-Symmetric Quasi-Newton Method for Minimax Problems. CoRR (2022)
Haoyue Wang, Haihao Lu, Rahul Mazumder
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. SIAM J. Optim. 32 (4) (2022)
Haihao Lu
-resolution ODE framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems. Math. Program. 194 (1) (2022)
Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni, Balasubramanian Sivan
From Online Optimization to PID Controllers: Mirror Descent with Momentum. CoRR (2022)
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. ALT (2022)
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)
Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni
Regularized Online Allocation Problems: Fairness and Beyond. ICML (2021)
Haihao Lu, Jinwen Yang
Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts. CoRR (2021)
Haihao Lu, Robert M. Freund
Generalized stochastic Frank-Wolfe algorithm with stochastic "substitute" gradient for structured convex optimization. Math. Program. 187 (1) (2021)
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. CoRR (2020)
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni
The Landscape of Nonconvex-Nonconcave Minimax Optimization. CoRR (2020)
Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni
Regularized Online Allocation Problems: Fairness and Beyond. CoRR (2020)
Haihao Lu, Rahul Mazumder
Randomized Gradient Boosting Machine. SIAM J. Optim. 30 (4) (2020)
Kenji Kawaguchi, Haihao Lu
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. AISTATS (2020)
Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni
Dual Mirror Descent for Online Allocation Problems. ICML (2020)
Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. NeurIPS (2020)
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
Accelerating Gradient Boosting Machines. AISTATS (2020)
Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. CoRR (2020)
Haihao Lu
)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems. CoRR (2020)
Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni
Contextual Reserve Price Optimization in Auctions. CoRR (2020)
Haihao Lu
"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent. INFORMS J. Optim. 1 (4) (2019)
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
Accelerating Gradient Boosting Machine. CoRR (2019)
Kenji Kawaguchi, Haihao Lu
A Stochastic First-Order Method for Ordered Empirical Risk Minimization. CoRR (2019)
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. CoRR (2018)
Haihao Lu, Robert M. Freund, Yurii E. Nesterov
Relatively Smooth Convex Optimization by First-Order Methods, and Applications. SIAM J. Optim. 28 (1) (2018)
Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab S. Mirrokni
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems. CoRR (2018)
Haihao Lu, Robert M. Freund, Vahab S. Mirrokni
Accelerating Greedy Coordinate Descent Methods. ICML (2018)
Jonathan Amar, Nicholas Renegar, Haihao Lu
Near-Optimal Online Knapsack Strategy for Real-Time Bidding in Internet Advertising. CoRR (2018)
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. ICML (2018)
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) (2018)
Haihao Lu, Rahul Mazumder
Randomized Gradient Boosting Machine. CoRR (2018)
Haihao Lu, Kenji Kawaguchi
Depth Creates No Bad Local Minima. CoRR (2017)