Search Results for author: Andrés Gómez

Found 12 papers, 4 papers with code

Robust support vector machines via conic optimization

no code implementations2 Feb 2024 Valentina Cepeda, Andrés Gómez, Shaoning Han

We consider the problem of learning support vector machines robust to uncertainty.

Learning Optimal Classification Trees Robust to Distribution Shifts

no code implementations26 Oct 2023 Nathan Justin, Sina Aghaei, Andrés Gómez, Phebe Vayanos

We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.

Classification Robust classification

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

1 code implementation28 Jul 2023 Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos

ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions.


Outlier detection in regression: conic quadratic formulations

no code implementations12 Jul 2023 Andrés Gómez, José Neto

In many applications, when building linear regression models, it is important to account for the presence of outliers, i. e., corrupted input data points.

Outlier Detection regression

Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression

no code implementations26 Jun 2023 Ryan Cory-Wright, Andrés Gómez

Across a suite of 13 real datasets, a calibrated version of our procedure improves the test set error by an average of 4% compared to cross-validating without confidence adjustment.


Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy

1 code implementation24 Jan 2022 Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos

The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algorithms.

Classification Fairness

On the convex hull of convex quadratic optimization problems with indicators

no code implementations2 Jan 2022 Linchuan Wei, Alper Atamtürk, Andrés Gómez, Simge Küçükyavuz

We show that a convex hull description of the associated mixed-integer set in an extended space with a quadratic number of additional variables consists of a single positive semidefinite constraint (explicitly stated) and linear constraints.

Learning Optimal Prescriptive Trees from Observational Data

1 code implementation31 Aug 2021 Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos

We consider the problem of learning an optimal prescriptive tree (i. e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data.


Strong Optimal Classification Trees

3 code implementations29 Mar 2021 Sina Aghaei, Andrés Gómez, Phebe Vayanos

To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees.

Binary Classification Classification +2

Safe Screening Rules for $\ell_0$-Regression

no code implementations19 Apr 2020 Alper Atamtürk, Andrés Gómez

We give safe screening rules to eliminate variables from regression with $\ell_0$ regularization or cardinality constraint.


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