Provable Gradient Editing of Deep Neural Networks

Tao, Zhe and Thakur, Aditya V.
Advances in Neural Information Processing Systems : Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

In explainable AI, DNN gradients are used to interpret the prediction; in safety-critical control systems, gradients could encode safety constraints; in scientific-computing applications, gradients could encode physical invariants. While recent work on provable editing of DNNs has focused on input-output constraints, the problem of enforcing hard constraints on DNN gradients remains unaddressed. We present ProGrad, the first efficient approach for editing the parameters of a DNN to provably enforce hard constraints on the DNN gradients.

@inproceedings{NeurIPS2025,
  author = {Tao, Zhe and Thakur, Aditya V.},
  year = {2025},
  title = {Provable Gradient Editing of Deep Neural Networks},
  booktitle = {Advances in Neural Information Processing Systems : Annual Conference on Neural Information Processing Systems (NeurIPS)},
  note = {To appear}
}