Regularizers that penalize big-M constants, unstable neurons, and per-sample LP relaxation gaps during neural network training reduce MILP solve times by up to four orders of magnitude while preserving surrogate accuracy.
arXiv preprint arXiv:2511.09400 (2025)
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SafeAdapt certifies a Rashomon set of safe policies from demonstration data and projects updates from arbitrary RL algorithms onto it to guarantee preservation of safety on source tasks.
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Relaxation-Informed Training of Neural Network Surrogate Models
Regularizers that penalize big-M constants, unstable neurons, and per-sample LP relaxation gaps during neural network training reduce MILP solve times by up to four orders of magnitude while preserving surrogate accuracy.
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SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
SafeAdapt certifies a Rashomon set of safe policies from demonstration data and projects updates from arbitrary RL algorithms onto it to guarantee preservation of safety on source tasks.