Dimitris Bertsimas

research

∙ 07/23/2023

A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

We propose an approach based on machine learning to solve two-stage line...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/23/2023

Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach

We propose a machine learning approach to the optimal control of multicl...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/05/2023

Compressed Sensing: A Discrete Optimization Approach

We study the Compressed Sensing (CS) problem, which is the problem of fi...

0 Dimitris Bertsimas, et al. ∙

research

∙ 05/26/2023

Improving Stability in Decision Tree Models

Owing to their inherently interpretable structure, decision trees are co...

0 Dimitris Bertsimas, et al. ∙

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∙ 05/25/2023

Patient Outcome Predictions Improve Operations at a Large Hospital Network

Problem definition: Access to accurate predictions of patients' outcomes...

0 Liangyuan Na, et al. ∙

research

∙ 05/20/2023

Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions

Low-rank matrix completion consists of computing a matrix of minimal com...

0 Dimitris Bertsimas, et al. ∙

research

∙ 05/02/2023

Finding Neurons in a Haystack: Case Studies with Sparse Probing

Despite rapid adoption and deployment of large language models (LLMs), t...

0 Wes Gurnee, et al. ∙

research

∙ 04/09/2023

Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach

Accurate time series forecasting is critical for a wide range of problem...

0 Dimitris Bertsimas, et al. ∙

research

∙ 03/22/2023

Reducing Air Pollution through Machine Learning

This paper presents a data-driven approach to mitigate the effects of ai...

0 Dimitris Bertsimas, et al. ∙

research

∙ 03/11/2023

Multistage Stochastic Optimization via Kernels

We develop a non-parametric, data-driven, tractable approach for solving...

0 Dimitris Bertsimas, et al. ∙

research

∙ 01/29/2023

Global Flood Prediction: a Multimodal Machine Learning Approach

Flooding is one of the most destructive and costly natural disasters, an...

0 Cynthia Zeng, et al. ∙

research

∙ 10/15/2022

Distributionally Robust Causal Inference with Observational Data

We consider the estimation of average treatment effects in observational...

0 Dimitris Bertsimas, et al. ∙

research

∙ 10/12/2022

Sparse PCA: a Geometric Approach

We consider the problem of maximizing the variance explained from a data...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/21/2022

TabText: a Systematic Approach to Aggregate Knowledge Across Tabular Data Structures

Processing and analyzing tabular data in a productive and efficient way ...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/01/2022

Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization

Discovering governing equations of complex dynamical systems directly fr...

0 Dimitris Bertsimas, et al. ∙

research

∙ 02/25/2022

Integrated multimodal artificial intelligence framework for healthcare applications

Artificial intelligence (AI) systems hold great promise to improve healt...

0 Luis R Soenksen, et al. ∙

research

∙ 12/17/2021

A Robust Optimization Approach to Deep Learning

Many state-of-the-art adversarial training methods leverage upper bounds...

0 Dimitris Bertsimas, et al. ∙

research

∙ 11/04/2021

Mixed-Integer Optimization with Constraint Learning

We establish a broad methodological foundation for mixed-integer optimiz...

0 Donato Maragno, et al. ∙

research

∙ 10/29/2021

Holistic Deep Learning

There is much interest in deep learning to solve challenges that arise i...

56 Dimitris Bertsimas, et al. ∙

research

∙ 09/26/2021

Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

We study the Sparse Plus Low Rank decomposition problem (SLR), which is ...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/03/2021

The Price of Diversity

Systemic bias with respect to gender, race and ethnicity, often unconsci...

0 Hari Bandi, et al. ∙

research

∙ 06/01/2021

Pricing Algorithmic Insurance

As machine learning algorithms start to get integrated into the decision...

0 Dimitris Bertsimas, et al. ∙

research

∙ 05/12/2021

A new perspective on low-rank optimization

A key question in many low-rank problems throughout optimization, machin...

0 Dimitris Bertsimas, et al. ∙

research

∙ 04/07/2021

Prediction with Missing Data

Missing information is inevitable in real-world data sets. While imputat...

0 Dimitris Bertsimas, et al. ∙

research

∙ 03/03/2021

Stochastic Cutting Planes for Data-Driven Optimization

We introduce a stochastic version of the cutting-plane method for a larg...

17 Dimitris Bertsimas, et al. ∙

research

∙ 02/22/2021

Slowly Varying Regression under Sparsity

We consider the problem of parameter estimation in slowly varying regres...

0 Dimitris Bertsimas, et al. ∙

research

∙ 12/08/2020

Optimal Survival Trees

Tree-based models are increasingly popular due to their ability to ident...

0 Dimitris Bertsimas, et al. ∙

research

∙ 11/11/2020

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a machine learning (ML) framework for tropical cycl...

0 Leonard Boussioux, et al. ∙

research

∙ 09/22/2020

Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints

We propose a framework for modeling and solving low-rank optimization pr...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/17/2020

Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme

We present a novel approach for the problem of frequency estimation in d...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/30/2020

From predictions to prescriptions: A data-driven response to COVID-19

The COVID-19 pandemic has created unprecedented challenges worldwide. St...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/11/2020

The Backbone Method for Ultra-High Dimensional Sparse Machine Learning

We present the backbone method, a generic framework that enables sparse ...

0 Dimitris Bertsimas, et al. ∙

research

∙ 05/11/2020

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

Sparse principal component analysis (PCA) is a popular dimensionality re...

0 Dimitris Bertsimas, et al. ∙

research

∙ 10/21/2019

Fast Exact Matrix Completion: A Unifying Optimization Framework

We consider the problem of matrix completion of rank k on an n× m matrix...

28 Dimitris Bertsimas, et al. ∙

research

∙ 10/18/2019

Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach

Current clinical practice guidelines for managing Coronary Artery Diseas...

31 Dimitris Bertsimas, et al. ∙

research

∙ 10/08/2019

On Polyhedral and Second-Order-Cone Decompositions of Semidefinite Optimization Problems

We study a cutting-plane method for semidefinite optimization problems (...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/17/2019

Dynamic optimization with side information

We present a data-driven framework for incorporating side information in...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/08/2019

Optimal Explanations of Linear Models

When predictive models are used to support complex and important decisio...

2 Dimitris Bertsimas, et al. ∙

research

∙ 07/08/2019

The Price of Interpretability

When quantitative models are used to support decision-making on complex ...

4 Dimitris Bertsimas, et al. ∙

research

∙ 07/04/2019

Online Mixed-Integer Optimization in Milliseconds

We propose a method to solve online mixed-integer optimization (MIO) pro...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/03/2019

A unified approach to mixed-integer optimization: Nonlinear formulations and scalable algorithms

We propose a unified framework to address a family of classical mixed-in...

0 Dimitris Bertsimas, et al. ∙

research

∙ 06/25/2019

Certifiably Optimal Sparse Inverse Covariance Estimation

We consider the maximum likelihood estimation of sparse inverse covarian...

0 Dimitris Bertsimas, et al. ∙

research

∙ 04/26/2019

From Predictions to Prescriptions in Multistage Optimization Problems

In this paper, we introduce a framework for solving finite-horizon multi...

0 Dimitris Bertsimas, et al. ∙

research

∙ 02/18/2019

Sparse Regression: Scalable algorithms and empirical performance

In this paper, we review state-of-the-art methods for feature selection ...

0 Dimitris Bertsimas, et al. ∙

research

∙ 02/08/2019

Accounting for Significance and Multicollinearity in Building Linear Regression Models

We derive explicit Mixed Integer Optimization (MIO) constraints, as oppo...

0 Dimitris Bertsimas, et al. ∙

research

∙ 12/17/2018

Interpretable Matrix Completion: A Discrete Optimization Approach

We consider the problem of matrix completion with side information on an...

0 Dimitris Bertsimas, et al. ∙

research

∙ 12/03/2018

Interpretable Clustering via Optimal Trees

State-of-the-art clustering algorithms use heuristics to partition the f...

0 Dimitris Bertsimas, et al. ∙

research

∙ 12/02/2018

Imputation of Clinical Covariates in Time Series

Missing data is a common problem in real-world settings and particularly...

0 Dimitris Bertsimas, et al. ∙

research

∙ 07/11/2018

Optimization over Continuous and Multi-dimensional Decisions with Observational Data

We consider the optimization of an uncertain objective over continuous a...

0 Dimitris Bertsimas, et al. ∙

research

∙ 11/27/2017

Bootstrap Robust Prescriptive Analytics

We address the problem of prescribing an optimal decision in a framework...

0 Dimitris Bertsimas, et al. ∙

Dimitris Bertsimas

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