88 citations · 120 across the 9 of their papers we have counts for
9 papers
Holistic Deep Learning
Dimitris Bertsimas, Kimberly Villalobos Carballo, Léonard Boussioux +3
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performa…
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…
Slowly Varying Regression under Sparsity
Dimitris Bertsimas, Vassilis Digalakis, Michael Linghzi Li +1
We present the framework of slowly varying regression under sparsity, allowing sparse regression models to exhibit slow and sparse variations. The problem of parameter estimation i…
Where to locate COVID-19 mass vaccination facilities?
Dimitris Bertsimas, Vassilis Digalakis, Alexander Jacquillat +2
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to…
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…