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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...
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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...
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06/05/2023
Compressed Sensing: A Discrete Optimization Approach
We study the Compressed Sensing (CS) problem, which is the problem of fi...
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05/26/2023
Improving Stability in Decision Tree Models
Owing to their inherently interpretable structure, decision trees are co...
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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...
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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...
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05/02/2023
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Despite rapid adoption and deployment of large language models (LLMs), t...
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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...
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03/22/2023
Reducing Air Pollution through Machine Learning
This paper presents a data-driven approach to mitigate the effects of ai...
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03/11/2023
Multistage Stochastic Optimization via Kernels
We develop a non-parametric, data-driven, tractable approach for solving...
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01/29/2023
Global Flood Prediction: a Multimodal Machine Learning Approach
Flooding is one of the most destructive and costly natural disasters, an...
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10/15/2022
Distributionally Robust Causal Inference with Observational Data
We consider the estimation of average treatment effects in observational...
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10/12/2022
Sparse PCA: a Geometric Approach
We consider the problem of maximizing the variance explained from a data...
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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 ...
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06/01/2022
Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization
Discovering governing equations of complex dynamical systems directly fr...
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02/25/2022
Integrated multimodal artificial intelligence framework for healthcare applications
Artificial intelligence (AI) systems hold great promise to improve healt...
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12/17/2021
A Robust Optimization Approach to Deep Learning
Many state-of-the-art adversarial training methods leverage upper bounds...
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11/04/2021
Mixed-Integer Optimization with Constraint Learning
We establish a broad methodological foundation for mixed-integer optimiz...
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10/29/2021
Holistic Deep Learning
There is much interest in deep learning to solve challenges that arise i...
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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 ...
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07/03/2021
The Price of Diversity
Systemic bias with respect to gender, race and ethnicity, often unconsci...
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06/01/2021
Pricing Algorithmic Insurance
As machine learning algorithms start to get integrated into the decision...
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05/12/2021
A new perspective on low-rank optimization
A key question in many low-rank problems throughout optimization, machin...
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04/07/2021
Prediction with Missing Data
Missing information is inevitable in real-world data sets. While imputat...
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03/03/2021
Stochastic Cutting Planes for Data-Driven Optimization
We introduce a stochastic version of the cutting-plane method for a larg...
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02/22/2021
Slowly Varying Regression under Sparsity
We consider the problem of parameter estimation in slowly varying regres...
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12/08/2020
Optimal Survival Trees
Tree-based models are increasingly popular due to their ability to ident...
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11/11/2020
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
This paper describes a machine learning (ML) framework for tropical cycl...
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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...
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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...
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06/30/2020
From predictions to prescriptions: A data-driven response to COVID-19
The COVID-19 pandemic has created unprecedented challenges worldwide. St...
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06/11/2020
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
We present the backbone method, a generic framework that enables sparse ...
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05/11/2020
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Sparse principal component analysis (PCA) is a popular dimensionality re...
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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...
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10/18/2019
Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach
Current clinical practice guidelines for managing Coronary Artery Diseas...
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10/08/2019
On Polyhedral and Second-Order-Cone Decompositions of Semidefinite Optimization Problems
We study a cutting-plane method for semidefinite optimization problems (...
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07/17/2019
Dynamic optimization with side information
We present a data-driven framework for incorporating side information in...
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07/08/2019
Optimal Explanations of Linear Models
When predictive models are used to support complex and important decisio...
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07/08/2019
The Price of Interpretability
When quantitative models are used to support decision-making on complex ...
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07/04/2019
Online Mixed-Integer Optimization in Milliseconds
We propose a method to solve online mixed-integer optimization (MIO) pro...
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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...
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06/25/2019
Certifiably Optimal Sparse Inverse Covariance Estimation
We consider the maximum likelihood estimation of sparse inverse covarian...
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04/26/2019
From Predictions to Prescriptions in Multistage Optimization Problems
In this paper, we introduce a framework for solving finite-horizon multi...
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02/18/2019
Sparse Regression: Scalable algorithms and empirical performance
In this paper, we review state-of-the-art methods for feature selection ...
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02/08/2019
Accounting for Significance and Multicollinearity in Building Linear Regression Models
We derive explicit Mixed Integer Optimization (MIO) constraints, as oppo...
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12/17/2018
Interpretable Matrix Completion: A Discrete Optimization Approach
We consider the problem of matrix completion with side information on an...
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12/03/2018
Interpretable Clustering via Optimal Trees
State-of-the-art clustering algorithms use heuristics to partition the f...
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12/02/2018
Imputation of Clinical Covariates in Time Series
Missing data is a common problem in real-world settings and particularly...
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07/11/2018
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
We consider the optimization of an uncertain objective over continuous a...
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11/27/2017