Preprints
Data-driven cold starting of good reservoirs
L. Grigoryeva, B. Hamzi, F. P. Kemeth, Y. Kevrekidis, Manjunath G, J.-P. Ortega, M. J. Steynberg. 2024+
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Memory of recurrent networks: Do we compute it right?
G. Ballarin, L. Grigoryeva, and J.-P. Ortega. 2023+
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Infinite-dimensional reservoir computing.
G. Gonon, L. Grigoryeva, and J.-P. Ortega. 2023+
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Reservoir kernels and Volterra series.
G. Gonon, L. Grigoryeva, and J.-P. Ortega. 2022+
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Option pricing and hedging with one-step Kalman filtered factors in non-affine stochastic volatility model.
A. Badescu, L. Grigoryeva, and J.-P. Ortega. 2017+
Singular regression with homoscedastic residuals: generalization error with estimated parameters.
L. Grigoryeva, J.-P. Ortega. 2016+
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Non-scalar GARCH models: Composite likelihood estimation and empirical model comparisons.
L. Bauwens, L. Grigoryeva, and J.-P. Ortega. 2016+
Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals.
L. Grigoryeva, J. Henriques, and J.-P. Ortega. 2015+
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Publications
Learning of Dynamic Processes and Dynamical Systems
Tracing curves in the plane: Geometric-invariant learning from human demonstrations.
H. Turlapati, L. Grigoryeva, J.-P. Ortega, and D. Campolo. 2024.
PLoS ONE, 19(2): e0294046
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Learning strange attractors with reservoir systems.
L. Grigoryeva, A. Hart, and J.-P. Ortega. 2023.
Nonlinearity, 36(9): 4674.
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Approximation bounds for random neural networks and reservoir systems.
L. Gonon, L. Grigoryeva, and J.-P. Ortega. 2023.
The Annals of Applied Probability, 33(1), 28-69.
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Dimension reduction in recurrent networks by canonicalization.
L. Grigoryeva and J.-P. Ortega. 2021.
Journal of Geometric Mechanics, 13(4): 647-677.
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Discrete-time signatures and randomness in reservoir computing.
C. Cuchiero, L. Gonon, L. Grigoryeva, J.-P. Ortega, and J. Teichmann. 2021.
IEEE Transactions on Neural Networks and Learning Systems, 33(11), 6321-6330.
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Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem.
L. Grigoryeva, A. Hart, and J.-P. Ortega. 2021.
Physical Review E, 103, 062204.
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Risk bounds for reservoir computing.
L. Gonon, L. Grigoryeva, and J.-P. Ortega. 2020.
Journal of Machine Learning Research (JMLR), 21(1), 9684–9744.
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Memory and forecasting capacities of nonlinear recurrent networks.
L. Gonon, L. Grigoryeva, and J.-P. Ortega. 2020.
Physica D, 414, 132721, 1-13.
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Differentiable reservoir computing.
L. Grigoryeva and J.-P. Ortega. 2019.
Journal of Machine Learning Research (JMLR), 20(179), 1-62.
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Echo state networks are universal.
L. Grigoryeva and J.-P. Ortega. 2018.
Neural Networks, 108, 495-508.
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Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems.
L. Grigoryeva and J.-P. Ortega. 2018.
Journal of Machine Learning Research (JMLR), 19(1), 892–931.
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Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals.
L. Grigoryeva, J. Henriques, L. Larger, and J.-P. Ortega. 2016.
Neural Computation. 28(7), 1411-1451.
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Reservoir computing: Information processing of stationary signals.
L. Grigoryeva, J. Henriques, and J.-P. Ortega. 2016.
Proceedings of the 19th IEEE International Conference on Computational Science and Engineering.
Best Paper Award
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Time-delay reservoir computers and high-speed information processing capacity.
L. Grigoryeva, J. Henriques, L. Larger, and J.-P. Ortega. 2016.
Proceedings of the 19th IEEE International Conference on Computational Science and Engineering.
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Optimal nonlinear information processing capacity in delay-based reservoir computers.
L. Grigoryeva, J. Henriques, L. Larger, and J.-P. Ortega. 2015.
Scientific Reports (Nature Publishing Group), 5(12858), 1-11.
doi pdf supplement poster
Time Series Forecasting and Financial Econometrics
Reservoir computing for macroeconomic forecasting with mixed frequency data.
G. Ballarin, P. Dellaportas, L. Grigoryeva, M. Hirt, S. van Huellen, and J.-P. Ortega. 2023.
To appear in the International Journal of Forecasting.
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Volatility forecasting using global stochastic financial trends extracted from non-synchronous data.
L. Grigoryeva, J.-P. Ortega, and A. Peresetsky. 2018.
Econometrics and Statistics, 5, 67–82.
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Estimation and empirical performance of non-scalar dynamic conditional correlation models.
L. Bauwens, L. Grigoryeva, and J.-P. Ortega. 2016.
Computational Statistics & Data Analysis, 100, 17-36.
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Asymptotic forecasting error evaluation for estimated temporally aggregated linear processes.
L. Grigoryeva and J.-P. Ortega. 2015.
International Journal of Computational Economics and Econometrics (IJCEE), 5(3), 289-318.
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Hybrid forecasting with estimated temporally aggregated linear processes.
L. Grigoryeva and J.-P. Ortega. 2014.
Journal of Forecasting, 33, 577-595.
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Physiological Time Series Analysis
Bedside evaluation of the functional organization of the auditory cortex in patients with disorders of consciousness.
J. Henriques, L. Pazart, L. Grigoryeva, E. Muzart, Y. Beaussant, E. Haffen, T. Moulin, R. Aubry, J.-P. Ortega, and D. Gabriel. 2016.
PLOS ONE, 11(1): e0146788.
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Protocol design challenges in the detection of awareness in aware subjects using EEG signals.
J. Henriques, D. Gabriel, L. Grigoryeva, E. Haffen, T. Moulin, R. Aubry, L. Pazart, J.-P. Ortega. 2016.
Clinical EEG & Neuroscience, 47(4), 266-275.
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Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions.
D. Gabriel, J. Henriques, A. Comte, L. Grigoryeva, J.-P. Ortega, E. Cretin, G. Brunotte, E. Haffen, T. Moulin, R. Aubry, L. Pazart. 2015.
Neuroscience, 290, 435-444.
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EEG- and fMRI-based communication tools in disorders of consciousness: which is the most reliable method?
D. Gabriel, A. Comte, J. Henriques, E. Magnin, L. Grigoryeva, J.-P. Ortega, E. Haffen, T. Moulin, L. Pazart, R. Aubry. 2013.
Clinical EEG and Neuroscience, 44(4), E111.
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Modeling of Physical Systems
Stability of Hamiltonian relative equilibria in symmetric magnetically confined rigid bodies.
L. Grigoryeva, J.-P. Ortega, and S. Zub. 2014.
Journal of Geometric Mechanics, 6(3), 373-415.
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A dynamical model of a free body in central and non-central physical fields and its Maple-analysis.
L. V. Grygor’yeva. 2008.
Bulletin of the University of Kyiv (Series: Physics and Mathematics), 2, 61-67. (in Ukrainian)
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Maple-exploring of a free flywheel suspended by the superconductive bearing.
L. V. Grygor’yeva. 2008.
Bulletin of the University of Kyiv (Series: Physics and Mathematics), 1, Kyiv: P. 75–80.
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Capabilities of the system Maple in studying dynamic systems of magnetically interacting free bodies.
L. V. Grigor’eva, V. V. Kozorez, and S. I. Lyashko. 2007.
Cybernetics and Systems Analysis, 43(6), Springer New York: 912-916. (Translated from Kibernetika i Sistemnyi Analiz, 6, 178-183, 2007).
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The dynamic problem of two free cylindrical magnets and its Maple-modelling.
L. V. Grygor’yeva, V. V. Kozorez, A. V., Kozorez, and S. I. Lyashko. 2007.
Bulletin of the National Academy of Sciences of Ukraine, 11, 41-47. (in Ukrainian)
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Maple-modeling of dynamics of a rigid body with a fixed point in the field of magnetic and electric forces.
L. V. Grygor’yeva, V. V. Kozorez, and S. I. Lyashko. 2007.
Bulletin of the National Academy of Sciences of Ukraine, 8, 45-48. (in Ukrainian).
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Maple-exploring of superconductive levitation in circle-dipole system (MPW in dipole due to circle).
Kozoriz, V. V., Lyashko, S. I., Tkachenko, R. L., Grigoryeva, L. V. 2007.
Journal of Applied and Computational Mathematics, 1(94), 48-55.
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