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Award ID contains: 1834701

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  1. ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available 
  2. ; ; ; ; ; (, 6th Annual Learning for Dynamics & Control Conference (L4DC'24))
    Accepted Manuscript Full Text Available
  3. ; ; ; (, 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'24))
    Full Text Available 
  4. ; ; ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    We present REGLO, a novel methodology for repairing pretrained neural networks to satisfy global robustness and individual fairness properties. A neural network is said to be globally robust with respect to a given input region if and only if all the input points in the region are locally robust. This notion of global robustness also captures the notion of individual fairness as a special case. We prove that any counterexample to a global robustness property must exhibit a corresponding large gradient. For ReLU networks, this result allows us to efficiently identify the linear regions that violate a given global robustness property. By formulating and solving a suitable robust convex optimization problem, REGLO then computes a minimal weight change that will provably repair these violating linear regions. 
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    Full Text Available 
  5. ; ; ; ; ; ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
    Full Text Available 
  6. ; ; ; (, ACM)
  7. ; ; ; ; (, IEEE/CVF International Conference on Computer Vision (ICCV’23))
  8. ; ; ; ; ; ; (, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’23))
  9. ; ; ; ; ; (, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’23))
  10. ; ; ; ; ; ; ; (, IEEE)