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Publications
Here is a link to my Google Scholar profile.
Working Papers
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems, with Santiago Balseiro and Vahab Mirrokni. (A preliminary version was accepted in ICML 2020). [arXiv]
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems, Benjamin Grimmer, Haihao Lu, Pratik Worah and Vahab Mirrokni. [arXiv]
Regularized Online Allocation Problems: Fairness and Beyond, with Santiago Balseiro and Vahab Mirrokni. [arXiv]
The Landscape of Nonconvex-Nonconcave Minimax Optimization, Benjamin Grimmer, Haihao Lu, Pratik Worah and Vahab Mirrokni. [arXiv]
An O(s^r)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems, Haihao Lu. [arXiv] [slides]
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems, Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu and Vahab Mirrokni (A preliminary version was accepted in ICML 2018). [arXiv]
Journal Publications (reverse chronological order)
Randomized Gradient Boosting Machines, Haihao Lu and Rahul Mazumder, to appear in SIAM Journal on Optimization. [arXiv]
Generalized Stochastic Frank-Wolfe Algorithm with Stochastic 'Substitute' Gradient for Structured Convex Optimization, Haihao Lu and Robert M. Freund, to appear in Mathematical Programming. [arXiv] [slides]
“Relative-Continuity” for Non-Lipschitz Non-Smooth Convex Optimization using Stochastic (or Deterministic) Mirror Descent, Haihao Lu, INFORMS Journal on Optimization, 1.4 (2019): 288-303. [arXiv]
Relatively-Smooth Convex Optimization by First-Order Methods, and Applications, Haihao Lu, Robert M. Freund and Yurii Nesterov, SIAM Journal on Optimization 28(1), 333–354, 2018. [arXiv] [Link]
New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, via a Function Growth Condition Measure, Robert M. Freund and Haihao Lu, Mathematical Programming Vol. 170, No. 2: 445–477, 2018. [pdf] [arXiv] [Link] [slides]
Stochastic Linearization of Turbulent Dynamics of Dispersive Waves in Equilibrium and Non-equilibrium State, Shixiao W Jiang, Haihao Lu, Douglas Zhou and David Cai, New Journal of Physics 18.8 (2016): 083028. [pdf] [Link]
Renormalized Dispersion Relations of β-Fermi-Pasta-Ulam Chains in Equilibrium
and Nonequilibrium States, Shixiao W Jiang, Haihao Lu, Douglas Zhou and David Cai, Physical Review E 90.3 (2014): 032925. [pdf] [Link]
Conference Publications (reverse chronological order)
Contextual Reserve Price Optimization in Auctions, Joey Huchette, Haihao Lu, Hossein Esfandiari and Vahab Mirrokni, NeurIPS 2020. [arXiv]
Dual Mirror Descent for Online Allocation Problems, with Santiago Balseiro and Vahab Mirrokni, ICML 2020. [arXiv][Link]
A Stochastic First-Order Method for Ordered Empirical Risk Minimization, with Kenji Kawaguchi, AISTATS 2020. [arXiv][Link]
Accelerating Gradient Boosting Machines, Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva and Vahab Mirrokni, AISTATS 2020. [arXiv][Link]
Accelerating Greedy Coordinate Descent Methods, Haihao Lu, Robert M. Freund and Vahab Mirrokni, ICML 2018. [arXiv] [Link] [slides]
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions, Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki and Vahab Mirrokni, ICML 2018. [arXiv] [Link]
Technical Reports
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