Preprints
Nearest Neighbors GParareal: Improving scalability of Gaussian processes for parallel-in-time solvers.
G. Gattiglio, L. Grigoryeva, and M. Tamborrino. 2024+
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Forecasting causal dynamics with universal reservoirs.
L. Grigoryeva, J. Louw, and J.-P. Ortega. 2024+
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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 (organized by topics)
Learning of Dynamic Processes and Dynamical Systems
RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks.
G. Gattiglio, L. Grigoryeva, and M. Tamborrino. 2024.
To appear in Advances in Neural Information Processing Systems, 37 (NeurIPS 2024)
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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.
Physica D, 469, 134325.
pdf code
Memory of recurrent networks: Do we compute it right?
G. Ballarin, L. Grigoryeva, and J.-P. Ortega. 2024.
Journal of Machine Learning Research (JMLR), 25, 1-38.
pdf code
Infinite-dimensional reservoir computing.
G. Gonon, L. Grigoryeva, and J.-P. Ortega. 2024.
Neural Networks, 179, 106486.
doi pdf
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.
doi pdf code
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.
doi pdf
Dimension reduction in recurrent networks by canonicalization.
L. Grigoryeva and J.-P. Ortega. 2021.
Journal of Geometric Mechanics, 13(4): 647-677.
doi pdf
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.
doi pdf
Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem.
L. Grigoryeva, A. Hart, and J.-P. Ortega. 2021.
Physical Review E, 103, 062204.
doi pdf
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.
doi pdf
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.
doi pdf
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.
doi pdf
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
doi pdf
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.
doi pdf
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. 2024.
International Journal of Forecasting, 40(3), 1206-1237.
pdf code journal
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.
doi pdf
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.
doi pdf poster code
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.
doi pdf code
Hybrid forecasting with estimated temporally aggregated linear processes.
L. Grigoryeva and J.-P. Ortega. 2014.
Journal of Forecasting, 33, 577-595.
doi pdf
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.
doi pdf
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.
doi pdf
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.
doi poster
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.
doi pdf
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).
doi
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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