Training Safe Neural Networks with Global SDP Bounds
<|reference_start|>Training Safe Neural Networks with Global SDP Bounds: This paper presents a novel approach to training neural networks with formal safety guarantees using semidefinite programming (SDP) for verification. Our method focuses on verifying safety over large, high-dimensional input regions, addressing limitations of existing techniques that focus on adversarial robustness bounds. We introduce an ADMM-based training scheme for an accurate neural network classifier on the Adversarial Spheres dataset, achieving provably perfect recall with input dimensions up to $d=40$. This work advances the development of reliable neural network verification methods for high-dimensional systems, with potential applications in safe RL policies.<|reference_end|>
@article{soletskyi2024training,
    title={Training Safe Neural Networks with Global SDP Bounds},
    author={Roman Soletskyi and David "davidad" Dalrymple},
    journal={arXiv preprint arXiv:2409.09687},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09687},
    primaryClass={cs.LG cs.AI math.OC}
}