3 citations · 3 across the 1 of their papers we have counts for
11 papers
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
David Applegate, Mateo Díaz, Haihao Lu +1
We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The lit…
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologicall…
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Minimax optimization has become a central tool in machine learning with applications in robust optimization, reinforcement learning, GANs, etc. These applications are often nonconv…
Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari +1
We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side. Fi…
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
Kenji Kawaguchi, Haihao Lu
We propose a new stochastic optimization framework for empirical risk minimization problems such as those that arise in machine learning. The traditional approaches, such as (mini-…
Accelerating Gradient Boosting Machine
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva +1
Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine lea…