A comprehensive tutorial that synthesizes how mixed-integer optimization can encode interpretability, robustness, and fairness constraints into machine learning models.
A Mixed-Integer Programming Approach to Training Dense Neural Networks
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abstract
Artificial Neural Networks (ANNs) are prevalent machine learning models that are applied across various real-world classification tasks. However, training ANNs is time-consuming and the resulting models take a lot of memory to deploy. In order to train more parsimonious ANNs, we propose a novel mixed-integer programming (MIP) formulation for training fully-connected ANNs. Our formulations can account for both binary and rectified linear unit (ReLU) activations, and for the use of a log-likelihood loss. We present numerical experiments comparing our MIP-based methods against existing approaches and show that we are able to achieve competitive out-of-sample performance with more parsimonious models.
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cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Responsible Machine Learning via Mixed-Integer Optimization
A comprehensive tutorial that synthesizes how mixed-integer optimization can encode interpretability, robustness, and fairness constraints into machine learning models.