Bifurcation of relative equilibria in mechanical systems with symmetry. - Pascal Chossat; Debra Lewis; Juan-Pablo Ortega; Tudor S. Ratiu (Adv. Appl. Math., 2003)

Multivariate GARCH estimation via a Bregman-proximal trust-region method. - Stéphane Chrétien; Juan-Pablo Ortega (Comput. Stat. Data Anal., 2014)

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)

Non-Gaussian GARCH option pricing models and their diffusion limits. - Alexandru Badescu; Robert J. Elliott; Juan-Pablo Ortega (Eur. J. Oper. Res., 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)

Closed-form variance swap prices under general affine GARCH models and their continuous-time limits. - Alexandru Badescu; Zhenyu Cui; Juan-Pablo Ortega (Ann. Oper. Res., 2019)

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)

Reservoir Computing Universality With Stochastic Inputs. - Lukas Gonon; Juan-Pablo Ortega (IEEE Trans. Neural Networks Learn. Syst., 2020)

Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems. - Juan-Pablo Ortega; Daiying Yin (GSI (2), 2021)

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

Fading memory echo state networks are universal. - Lukas Gonon; Juan-Pablo Ortega (Neural Networks, 2021)

Transport in reservoir computing. - G. Manjunath; Juan-Pablo Ortega (CoRR, 2022)

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)

Guest Editorial Special Issue on New Frontiers in Extremely Efficient Reservoir Computing. - Gouhei Tanaka; Claudio Gallicchio; Alessio Micheli; Juan-Pablo Ortega; Akira Hirose 001 (IEEE Trans. Neural Networks Learn. Syst., 2022)

The Gaussian Kernel on the Circle and Spaces that Admit Isometric Embeddings of the Circle. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega (GSI (1), 2023)

Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds. - Marcel Hirt; Domenico Campolo; Victoria Leong; Juan-Pablo Ortega (CoRR, 2023)

Geometric Learning with Positively Decomposable Kernels. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega; Salem Said (CoRR, 2023)

Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach. - Nathaël Da Costa; Cyrus Mostajeran; Juan-Pablo Ortega; Salem Said (CoRR, 2023)

The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility. - Vishal Ramanathan; Mohammad Zaidi Ariffin; Guo Dong Goh; Guo Liang Goh; Mohammad Adhimas Rikat; Xing Xi Tan; Wai Yee Yeong; Juan-Pablo Ortega; Victoria Leong; Domenico Campolo (Sensors, 2023)

A Structure-Preserving Kernel Method for Learning Hamiltonian Systems. - Jianyu Hu; Juan-Pablo Ortega; Daiying Yin (CoRR, 2024)

State-Space Systems as Dynamic Generative Models. - Juan-Pablo Ortega; Florian Rossmannek (CoRR, 2024)

Fading memory and the convolution theorem. - Juan-Pablo Ortega; Florian Rossmannek (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)

Learnability of Linear Port-Hamiltonian Systems. - Juan-Pablo Ortega; Daiying Yin (J. Mach. Learn. Res., 2024)

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

Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives. - Marcel Hirt; Domenico Campolo; Victoria Leong; Juan-Pablo Ortega (Trans. Mach. Learn. Res., 2024)

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

Feedback-driven recurrent quantum neural network universality. - Lukas Gonon; Rodrigo Martínez-Peña; Juan-Pablo Ortega (CoRR, 2025)

A Global Structure-Preserving Kernel Method for the Learning of Poisson Systems. - Jianyu Hu; Juan-Pablo Ortega; Daiying Yin (J. Nonlinear Sci., 2025)