Stochastic nonlinear time series forecasting using time-delay reservoir computers: Performance and universality. - Lyudmila Grigoryeva; Julie Henriques; Laurent Larger; Juan-Pablo Ortega (Neural Networks, 2014)

Forecasting, filtering, and reconstruction of stochastic stationary signals using discrete-time reservoir computers. - Lyudmila Grigoryeva; Julie Henriques; Juan-Pablo Ortega (CoRR, 2015)

Time-Delay Reservoir Computers and High-Speed Information Processing Capacity. - Lyudmila Grigoryeva; Julie Henriques; Laurent Larger; Juan-Pablo Ortega (CSE/EUC/DCABES, 2016)

Reservoir Computing: Information Processing of Stationary Signals. - Lyudmila Grigoryeva; Julie Henriques; Juan-Pablo Ortega (CSE/EUC/DCABES, 2016)

Estimation and empirical performance of non-scalar dynamic conditional correlation models. - Luc Bauwens; Lyudmila Grigoryeva; Juan-Pablo Ortega (Comput. Stat. Data Anal., 2016)

Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals. - Lyudmila Grigoryeva; Julie Henriques; Laurent Larger; Juan-Pablo Ortega (Neural Comput., 2016)

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems. - Lyudmila Grigoryeva; Juan-Pablo Ortega (J. Mach. Learn. Res., 2018)

Echo state networks are universal. - Lyudmila Grigoryeva; Juan-Pablo Ortega (Neural Networks, 2018)

Risk bounds for reservoir computing. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2019)

Differentiable reservoir computing. - Lyudmila Grigoryeva; Juan-Pablo Ortega (J. Mach. Learn. Res., 2019)

Approximation Bounds for Random Neural Networks and Reservoir Systems. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2020)

Memory and forecasting capacities of nonlinear recurrent networks. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2020)

Dimension reduction in recurrent networks by canonicalization. - Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2020)

Learning strange attractors with reservoir systems. - Lyudmila Grigoryeva; Allen Hart; Juan-Pablo Ortega (CoRR, 2021)

Reservoir kernels and Volterra series. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2022)

Discrete-Time Signatures and Randomness in Reservoir Computing. - Christa Cuchiero; Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega; Josef Teichmann (IEEE Trans. Neural Networks Learn. Syst., 2022)

RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks. - Guglielmo Gattiglio; Lyudmila Grigoryeva; Massimiliano Tamborrino (NeurIPS, 2024)

Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers. - Guglielmo Gattiglio; Lyudmila Grigoryeva; Massimiliano Tamborrino (CoRR, 2024)

Infinite-dimensional next-generation reservoir computing. - Lyudmila Grigoryeva; Hannah Lim Jing Ting; Juan-Pablo Ortega (CoRR, 2024)

Memory of recurrent networks: Do we compute it right? - Giovanni Ballarin; Lyudmila Grigoryeva; Juan-Pablo Ortega (J. Mach. Learn. Res., 2024)

Infinite-dimensional reservoir computing. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (Neural Networks, 2024)

Memory Capacity of Nonlinear Recurrent Networks: Is it Informative? - Giovanni Ballarin; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2025)