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math.OC2026
Relaxation-Informed Training of Neural Network Surrogate Models
Calvin Tsay
ReLU neural networks trained as surrogate models can be embedded exactly in mixed-integer linear programs (MILPs), enabling global optimization over the learned function. The tract…
math.OC2026
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
Wei-Ting Tang, Akshay Kudva, Calvin Tsay +1
We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compac…
cs.LG2026
Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming
Philip Sosnin, Jodie Knapp, Fraser Kennedy +2
This work introduces a verification framework that provides both sound and complete guarantees for data poisoning attacks during neural network training. We formulate adversarial d…