Distributed Nonlinear Control Design Using Separable Control Contraction Metrics. - Humberto Stein Shiromoto; Max Revay; Ian R. Manchester (IEEE Trans. Control. Netw. Syst., 2019)

Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability. - Max Revay; Ian R. Manchester (L4DC, 2020)

Convex Sets of Robust Recurrent Neural Networks. - Max Revay; Ruigang Wang; Ian R. Manchester (CoRR, 2020)

Lipschitz Bounded Equilibrium Networks. - Max Revay; Ruigang Wang; Ian R. Manchester (CoRR, 2020)

Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial. - Ian R. Manchester; Max Revay; Ruigang Wang (CDC, 2021)

Recurrent Equilibrium Networks: Unconstrained Learning of Stable and Robust Dynamical Models. - Max Revay; Ruigang Wang; Ian R. Manchester (CDC, 2021)

A Convex Parameterization of Robust Recurrent Neural Networks. - Max Revay; Ruigang Wang; Ian R. Manchester (IEEE Control. Syst. Lett., 2021)

Convex Clustering for Autocorrelated Time Series. - Max Revay; Victor Solo (ICASSP, 2022)

Distributed Identification of Contracting and/or Monotone Network Dynamics. - Max Revay; Jack Umenberger; Ian R. Manchester (IEEE Trans. Autom. Control., 2022)

RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness. - Nicholas H. Barbara; Max Revay; Ruigang Wang; Jing Cheng; Ian R. Manchester (CoRR, 2023)

Learning Over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System. - Ruigang Wang; Nicholas Barbara; Max Revay; Ian R. Manchester (IEEE Control. Syst. Lett., 2023)

Recurrent Equilibrium Networks: Flexible Dynamic Models With Guaranteed Stability and Robustness. - Max Revay; Ruigang Wang; Ian R. Manchester (IEEE Trans. Autom. Control., 2024)