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Found 12 papers, 4 papers with code
Robust support vector machines via conic optimization
We consider the problem of learning support vector machines robust to uncertainty.
Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction
Quantum Recurrent Neural Networks (QRNNs) are robust candidates to model and predict future values in multivariate time series.
Learning Optimal Classification Trees Robust to Distribution Shifts
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
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
In many applications, when building linear regression models, it is important to account for the presence of outliers, i. e., corrupted input data points.
Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression
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
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.
On the convex hull of convex quadratic optimization problems with indicators
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
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.
Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features
However, there is still the challenge to obtain more characteristics from the objects detected in real-time.
Strong Optimal Classification Trees
To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees.
Safe Screening Rules for $\ell_0$-Regression
We give safe screening rules to eliminate variables from regression with $\ell_0$ regularization or cardinality constraint.
