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Found 5 papers, 1 papers with code
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions.
Counterfactual Explanations Using Optimization With Constraint Learning
To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature.
Why we do need Explainable AI for Healthcare
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology.
Bolstering Stochastic Gradient Descent with Model Building
Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems.
Discovering Classification Rules for Interpretable Learning with Linear Programming
In various applications such classification rules are considered to be interpretable by the decision makers.
