Haihao (Sean) Lu
Haihao (Sean) Lu
Assistant Professor, University of Chicago
Verified email at chicagobooth.edu - Homepage
Title
Cited by
Cited by
Year
Relatively smooth convex optimization by first-order methods, and applications
H Lu, RM Freund, Y Nesterov
SIAM Journal on Optimization 28 (1), 333-354, 2018
1642018
Depth creates no bad local minima
H Lu, K Kawaguchi
arXiv preprint arXiv:1702.08580, 2017
752017
“Relative Continuity” for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent
H Lu
Informs Journal on Optimization 1 (4), 288-303, 2019
362019
New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure
RM Freund, H Lu
Mathematical Programming 170 (2), 445-477, 2018
252018
Accelerating Greedy Coordinate Descent Methods
H Lu, R Freund, V Mirrokni
International Conference on Machine Learning, 3257-3266, 2018
212018
Generalized stochastic frank–wolfe algorithm with stochastic “substitute” gradient for structured convex optimization
H Lu, RM Freund
Mathematical Programming 187 (1), 317-349, 2021
192021
Accelerating Gradient Boosting Machines
H Lu, SP Karimireddy, N Ponomareva, V Mirrokni
International Conference on Artificial Intelligence and Statistics, 516-526, 2020
162020
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
S Wang, W Zhou, H Lu, A Maleki, V Mirrokni
International Conference on Machine Learning, 5228-5237, 2018
162018
Dual Mirror Descent for Online Allocation Problems
S Balseiro, H Lu, V Mirrokni
International Conference on Machine Learning, 613-628, 2020
11*2020
Randomized gradient boosting machine
H Lu, R Mazumder
SIAM Journal on Optimization 30 (4), 2780-2808, 2020
102020
Ordered SGD: A new stochastic optimization framework for empirical risk minimization
K Kawaguchi, H Lu
International Conference on Artificial Intelligence and Statistics, 669-679, 2020
62020
An -Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to the Linear Convergence of Minimax Problems
H Lu
arXiv preprint arXiv:2001.08826, 2020
6*2020
Renormalized dispersion relations of -Fermi-Pasta-Ulam chains in equilibrium and nonequilibrium states
WJ Shi-xiao, H Lu, D Zhou, D Cai
Physical Review E 90 (3), 032925, 2014
62014
Approximate leave-one-out for high-dimensional non-differentiable learning problems
S Wang, W Zhou, A Maleki, H Lu, V Mirrokni
arXiv preprint arXiv:1810.02716, 2018
52018
Stochastic linearization of turbulent dynamics of dispersive waves in equilibrium and non-equilibrium state
SW Jiang, H Lu, D Zhou, D Cai
New Journal of Physics 18 (8), 083028, 2016
52016
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
B Grimmer, H Lu, P Worah, V Mirrokni
4*2020
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
S Balseiro, H Lu, V Mirrokni
arXiv preprint arXiv:2011.10124, 2020
32020
Regularized Online Allocation Problems: Fairness and Beyond
S Balseiro, H Lu, V Mirrokni
arXiv preprint arXiv:2007.00514, 2020
32020
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
D Applegate, M Díaz, H Lu, M Lubin
arXiv preprint arXiv:2102.04592, 2021
22021
Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness
D Applegate, O Hinder, H Lu, M Lubin
arXiv preprint arXiv:2105.12715, 2021
12021
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Articles 1–20