7 citations · 7 across the 2 of their papers we have counts for
6 papers
Stochastic Cutting Planes for Data-Driven Optimization
Dimitris Bertsimas, Michael Lingzhi Li
We introduce a stochastic version of the cutting-plane method for a large class of data-driven Mixed-Integer Nonlinear Optimization (MINLO) problems. We show that under very weak a…
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
Michael Lingzhi Li, Meng Dong, Jiawei Zhou +1
Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been prop…
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion
Dimitris Bertsimas, Michael Lingzhi Li
We formulate the problem of matrix completion with and without side information as a non-convex optimization problem. We design fastImpute based on non-convex gradient descent and…
Duration-of-Stay Storage Assignment under Uncertainty
Michael Lingzhi Li, Elliott Wolf, Daniel Wintz
Optimizing storage assignment is a central problem in warehousing. Past literature has shown the superiority of the Duration-of-Stay (DoS) method in assigning pallets, but the meth…
Scalable Holistic Linear Regression
Dimitris Bertsimas, Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinear…
Interpretable Matrix Completion: A Discrete Optimization Approach
Dimitris Bertsimas, Michael Lingzhi Li
We consider the problem of matrix completion on an matrix. We introduce the problem of Interpretable Matrix Completion that aims to provide meaningful insights for the…