Depth Creates No Bad Local Minima. - Haihao Lu; Kenji Kawaguchi (CoRR, 2017)

Accelerating Greedy Coordinate Descent Methods. - Haihao Lu; Robert M. Freund; Vahab S. Mirrokni (ICML, 2018)

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. - Shuaiwen Wang; Wenda Zhou; Haihao Lu; Arian Maleki; Vahab S. Mirrokni (ICML, 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)

Near-Optimal Online Knapsack Strategy for Real-Time Bidding in Internet Advertising. - Jonathan Amar; Nicholas Renegar; Haihao Lu (CoRR, 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)

Relatively Smooth Convex Optimization by First-Order Methods, and Applications. - Haihao Lu; Robert M. Freund; Yurii E. Nesterov (SIAM J. Optim., 2018)

Accelerating Gradient Boosting Machine. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (CoRR, 2019)

A Stochastic First-Order Method for Ordered Empirical Risk Minimization. - Kenji Kawaguchi; Haihao Lu (CoRR, 2019)

"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent. - Haihao Lu (INFORMS J. Optim., 2019)

Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. - Kenji Kawaguchi; Haihao Lu (AISTATS, 2020)

Accelerating Gradient Boosting Machines. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (AISTATS, 2020)

Dual Mirror Descent for Online Allocation Problems. - Santiago R. Balseiro; Haihao Lu; Vahab S. Mirrokni (ICML, 2020)

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. - Joey Huchette; Haihao Lu; Hossein Esfandiari; Vahab S. Mirrokni (NeurIPS, 2020)

An O(sr)-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. - Joey Huchette; Haihao Lu; Hossein Esfandiari; Vahab S. Mirrokni (CoRR, 2020)

The Landscape of Nonconvex-Nonconcave Minimax Optimization. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab S. Mirrokni (CoRR, 2020)

Randomized Gradient Boosting Machine. - Haihao Lu; Rahul Mazumder (SIAM J. Optim., 2020)

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 (NeurIPS, 2021)

Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts. - Haihao Lu; Jinwen Yang (CoRR, 2021)

Generalized stochastic Frank-Wolfe algorithm with stochastic "substitute" gradient for structured convex optimization. - Haihao Lu; Robert M. Freund (Math. Program., 2021)

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab S. Mirrokni (ALT, 2022)

A J-Symmetric Quasi-Newton Method for Minimax Problems. - Azam Asl; Haihao Lu (CoRR, 2022)

From Online Optimization to PID Controllers: Mirror Descent with Momentum. - Santiago R. Balseiro; Haihao Lu; Vahab S. Mirrokni; Balasubramanian Sivan (CoRR, 2022)

An O(sr)-resolution ODE framework for understanding discrete-time algorithms and applications to the linear convergence of minimax problems. - Haihao Lu (Math. Program., 2022)

Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. - Haoyue Wang; Haihao Lu; Rahul Mazumder (SIAM J. Optim., 2022)

Online Ad Procurement in Non-stationary Autobidding Worlds. - Jason Cheuk Nam Liang; Haihao Lu; Baoyu Zhou (NeurIPS, 2023)

Joint Feedback Loop for Spend and Return-On-Spend Constraints. - Santiago R. Balseiro; Kshipra Bhawalkar; Zhe Feng 004; Haihao Lu; Vahab Mirrokni; Balasubramanian Sivan; Di Wang 005 (CoRR, 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)

The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab Mirrokni (Math. Program., 2023)

The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. - Santiago R. Balseiro; Haihao Lu; Vahab Mirrokni (Oper. Res., 2023)

A Field Guide for Pacing Budget and ROS Constraints. - Santiago R. Balseiro; Kshipra Bhawalkar; Zhe Feng 004; Haihao Lu; Vahab Mirrokni; Balasubramanian Sivan; Di Wang 005 (ICML, 2024)

MPAX: Mathematical Programming in JAX. - Haihao Lu; Zedong Peng; Jinwen Yang (CoRR, 2024)

Auto-Bidding and Auctions in Online Advertising: A Survey. - Gagan Aggarwal; Ashwinkumar Badanidiyuru; Santiago R. Balseiro; Kshipra Bhawalkar; Yuan Deng; Zhe Feng 004; Gagan Goel; Christopher Liaw; Haihao Lu; Mohammad Mahdian; Jieming Mao; Aranyak Mehta; et al. (SIGecom Exch., 2024)

On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems. - Saeed Hajizadeh; Haihao Lu; Benjamin Grimmer (INFORMS J. Optim., 2024)

A J-symmetric quasi-newton method for minimax problems. - Azam Asl; Haihao Lu; Jinwen Yang (Math. Program., 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., 2024)

Regularized Online Allocation Problems: Fairness and Beyond. - Santiago R. Balseiro; Haihao Lu; Vahab Mirrokni (Manuf. Serv. Oper. Manag., 2025)

On the geometry and refined rate of primal-dual hybrid gradient for linear programming. - Haihao Lu; Jinwen Yang (Math. Program., 2025)