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  • Beckers, Thomas, Seidman, Jacob H., et. al. “Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior”. Proceedings of the Conference on Decision and Control (CDC). IEEE, (2022).

  • Seidman, Jacob H., et al. “Robust deep learning as optimal control: Insights and convergence guarantees.” Learning for Dynamics and Control. PMLR, (2020)

  • Seidman, Jacob H., et al. “A control-theoretic approach to analysis and parameter selection of douglas–rachford splitting.” IEEE Control Systems Letters 4.1 (2019): 199-204.

  • Seidman, Jacob H., et al. “A chebyshev-accelerated primal-dual method for distributed optimization.” 2018 IEEE Conference on Decision and Control (CDC). IEEE, (2018).

*authors contributed equally