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cs.LG2025
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
Daniel Ovalle, Lorenz T. Biegler, Ignacio E. Grossmann +2
We propose Conformal Mixed-Integer Constraint Learning (C-MICL), a novel framework that provides probabilistic feasibility guarantees for data-driven constraints in optimization pr…
cs.LG2025
A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
Laurens R. Lueg, Victor Alves, Daniel Schicksnus +3
Scientific machine learning is an emerging field that broadly describes the combination of scientific computing and machine learning to address challenges in science and engineerin…