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07/28/2023
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
ODTLearn is an open-source Python package that provides methods for lear...
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07/25/2023
Solution Path of Time-varying Markov Random Fields with Discrete Regularization
We study the problem of inferring sparse time-varying Markov random fiel...
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07/12/2023
Outlier detection in regression: conic quadratic formulations
In many applications, when building linear regression models, it is impo...
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06/26/2023
Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression
Ridge regularized sparse regression involves selecting a subset of featu...
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01/24/2022
Learning Optimal Fair Classification Trees
The increasing use of machine learning in high-stakes domains – where pe...
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01/02/2022
On the convex hull of convex quadratic optimization problems with indicators
We consider the convex quadratic optimization problem with indicator var...
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08/31/2021
Learning Optimal Prescriptive Trees from Observational Data
We consider the problem of learning an optimal prescriptive tree (i.e., ...
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08/05/2021
Memory-Aware Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems
Sensing systems powered by energy harvesting have traditionally been des...
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07/27/2021
Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features
Object detection is a critical problem for the safe interaction between ...
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03/29/2021
Strong Optimal Classification Trees
Decision trees are among the most popular machine learning models and ar...
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02/06/2021
Scalable Inference of Sparsely-changing Markov Random Fields with Strong Statistical Guarantees
In this paper, we study the problem of inferring time-varying Markov ran...
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12/29/2020
Supermodularity and valid inequalities for quadratic optimization with indicators
We study the minimization of a rank-one quadratic with indicators and sh...
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06/30/2020
Ideal formulations for constrained convex optimization problems with indicator variables
Motivated by modern regression applications, in this paper, we study the...
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04/19/2020
Safe Screening Rules for ℓ_0-Regression
We give safe screening rules to eliminate variables from regression with...
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02/21/2020
Learning Optimal Classification Trees: Strong Max-Flow Formulations
We consider the problem of learning optimal binary classification trees....
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01/29/2019
Rank-one Convexification for Sparse Regression
Sparse regression models are increasingly prevalent due to their ease of...
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11/06/2018
Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
Signal estimation problems with smoothness and sparsity priors can be na...
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04/20/2017
