Academic Profile : Faculty
Prof Juan-Pablo Ortega Lahuerta
Associate Chair (Faculty)
Lee Soo Ying Professor of Mathematics
Professor, School of Physical & Mathematical Sciences - Division of Mathematical Sciences
External Links
Journal Articles
(Not applicable to NIE
staff as info will be
pulled from PRDS)
(Not applicable to NIE
staff as info will be
pulled from PRDS)
Ortega, J.-P. and Yin, D. [2024] Learnability of port-Hamiltonian systems. Journal of Machine Learning Research, 25, 1-56.
Ballarin, G., Dellaportas, P., Grigoryeva, L., Hirt, M., van Huellen, S., and Ortega, J.-P. [2024] Reservoir computing for macroeconomic forecasting with mixed frequency data. International Journal of Forecasting, 40, 1206-1237.
Audrino, F., Chassot, J., Huang, C., Knaus, M., Lechner, M., and Ortega, J.-P. [2024] How does post-earnings announcement sentiment affect firms’ dynamics? New evidence from causal machine learning. Journal of Financial Econometrics, 22(3), 575-604, doi.org/10.1093/jjfinec/nbac018.
Turlapati, H., Grigoryeva, H., Ortega, J.-P., and Campolo, D. [2024] Tracing curves in the plane: geometric-invariant learning from human demonstrations. PLOS ONE, 19(2):e0294046. DOI: 10.1371/journal.pone.0294046.
Grigoryeva, L., Hart, A., and Ortega, J.-P. [2023] Learning strange attractors with reservoir systems. Nonlinearity, 36, 4674-4708. DOI 10.1088/1361-6544/ace492.
Manjunath, G and Ortega, J.-P. [2023] Transport in reservoir computing. Physica D, 449, 133744, doi.org/10.1016/j.physd.2023.133744.
Martínez-Peña, R. and Ortega, J.-P. [2023] Quantum reservoir computing in finite dimensions. Physical Review E, 107(3), 035306, 0.1103/PhysRevE.107.035306.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2023] Approximation bounds for random neural networks and reservoir systems. The Annals of Applied Probability, 33(1), 28-69, doi.org/10.1214/22-AAP1806.
Ramanathan, V., Ariffin, M.Z., Goh, G.D., Goh, G.L., Rikat, M.A., Tan, X.X., Yeong, W.Y., Ortega, J.-P., Leong, V., and Campolo, D. [2023] Design and development of instrumented toys for social interactive assessment of infant cognitive flexibility. Sensors, 23(5), 2709. DOI: 10.3390/s23052709.
Joucla, C., Gabriel, D., Ortega, J.-P., and Haffen, E. [2022] Three simple steps to improve the interpretability of EEG-SVM studies. Journal of Neurophysiology, 128(6), 1375-1382. DOI: 10.1152/jn.00221.2022
Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., and Teichmann, J. [2021] Expressive power of randomized signature. NeurIPS Workshop 2021.
Grigoryeva, L., and Ortega, J.-P. [2021] Dimension reduction in recurrent networks by canonicalization. Journal of Geometric Mechanics, 13(4): 647-677. doi:10.3934/jgm.2021028.
Gonon, L. and Ortega, J.-P. [2021] Fading memory echo state networks are universal. Neural Networks, 138, 10-13.
Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., and Teichmann, J. [2021] Discrete-time signatures and randomness in reservoir computing. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2021.3076777.
Grigoryeva, L., Hart, A., and Ortega, J.-P. [2021] Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem. Physical Review E, 103, 062204.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Risk bounds for reservoir computing. Journal of Machine Learning Research, 21(240), 1-61.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Memory and forecasting capacities of nonlinear recurrent networks. Physica D, 414, 132721, 1-13.
Gonon, L. and Ortega, J.-P. [2020] Reservoir computing universality with stochastic inputs. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 100-112.
Grigoryeva, L. and Ortega, J.-P. [2019] Differentiable reservoir computing. Journal of Machine Learning Research, 20(179), 1-62.
Audrino, F., Kostrov, A., and Ortega, J.-P. [2019] Predicting U.S. bank failures with MIDAS logit models. Journal of Financial and Quantitative Analysis, 54(6), 2575-2603.
Badescu, A., Cui, Z., and Ortega, J.-P. [2019] Closed-form variance swap prices under general affine GARCH models and their continuous-time limits. Annals of Operations Research, 282, 27-57.
Grigoryeva, L. and Ortega, J.-P. [2018] Echo state networks are universal. Neural Networks, 108, 495-508.
Ballarin, G., Dellaportas, P., Grigoryeva, L., Hirt, M., van Huellen, S., and Ortega, J.-P. [2024] Reservoir computing for macroeconomic forecasting with mixed frequency data. International Journal of Forecasting, 40, 1206-1237.
Audrino, F., Chassot, J., Huang, C., Knaus, M., Lechner, M., and Ortega, J.-P. [2024] How does post-earnings announcement sentiment affect firms’ dynamics? New evidence from causal machine learning. Journal of Financial Econometrics, 22(3), 575-604, doi.org/10.1093/jjfinec/nbac018.
Turlapati, H., Grigoryeva, H., Ortega, J.-P., and Campolo, D. [2024] Tracing curves in the plane: geometric-invariant learning from human demonstrations. PLOS ONE, 19(2):e0294046. DOI: 10.1371/journal.pone.0294046.
Grigoryeva, L., Hart, A., and Ortega, J.-P. [2023] Learning strange attractors with reservoir systems. Nonlinearity, 36, 4674-4708. DOI 10.1088/1361-6544/ace492.
Manjunath, G and Ortega, J.-P. [2023] Transport in reservoir computing. Physica D, 449, 133744, doi.org/10.1016/j.physd.2023.133744.
Martínez-Peña, R. and Ortega, J.-P. [2023] Quantum reservoir computing in finite dimensions. Physical Review E, 107(3), 035306, 0.1103/PhysRevE.107.035306.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2023] Approximation bounds for random neural networks and reservoir systems. The Annals of Applied Probability, 33(1), 28-69, doi.org/10.1214/22-AAP1806.
Ramanathan, V., Ariffin, M.Z., Goh, G.D., Goh, G.L., Rikat, M.A., Tan, X.X., Yeong, W.Y., Ortega, J.-P., Leong, V., and Campolo, D. [2023] Design and development of instrumented toys for social interactive assessment of infant cognitive flexibility. Sensors, 23(5), 2709. DOI: 10.3390/s23052709.
Joucla, C., Gabriel, D., Ortega, J.-P., and Haffen, E. [2022] Three simple steps to improve the interpretability of EEG-SVM studies. Journal of Neurophysiology, 128(6), 1375-1382. DOI: 10.1152/jn.00221.2022
Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., and Teichmann, J. [2021] Expressive power of randomized signature. NeurIPS Workshop 2021.
Grigoryeva, L., and Ortega, J.-P. [2021] Dimension reduction in recurrent networks by canonicalization. Journal of Geometric Mechanics, 13(4): 647-677. doi:10.3934/jgm.2021028.
Gonon, L. and Ortega, J.-P. [2021] Fading memory echo state networks are universal. Neural Networks, 138, 10-13.
Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., and Teichmann, J. [2021] Discrete-time signatures and randomness in reservoir computing. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2021.3076777.
Grigoryeva, L., Hart, A., and Ortega, J.-P. [2021] Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem. Physical Review E, 103, 062204.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Risk bounds for reservoir computing. Journal of Machine Learning Research, 21(240), 1-61.
Gonon, L., Grigoryeva, L., and Ortega, J.-P. [2020] Memory and forecasting capacities of nonlinear recurrent networks. Physica D, 414, 132721, 1-13.
Gonon, L. and Ortega, J.-P. [2020] Reservoir computing universality with stochastic inputs. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 100-112.
Grigoryeva, L. and Ortega, J.-P. [2019] Differentiable reservoir computing. Journal of Machine Learning Research, 20(179), 1-62.
Audrino, F., Kostrov, A., and Ortega, J.-P. [2019] Predicting U.S. bank failures with MIDAS logit models. Journal of Financial and Quantitative Analysis, 54(6), 2575-2603.
Badescu, A., Cui, Z., and Ortega, J.-P. [2019] Closed-form variance swap prices under general affine GARCH models and their continuous-time limits. Annals of Operations Research, 282, 27-57.
Grigoryeva, L. and Ortega, J.-P. [2018] Echo state networks are universal. Neural Networks, 108, 495-508.
Books
(Not applicable to NIE
staff as info will be
pulled from PRDS)
(Not applicable to NIE
staff as info will be
pulled from PRDS)
Marsden, J. E., Misiolek, G., Ortega, J.-P., Perlmutter, M., and Ratiu, T. S. [2007] Hamiltonian Reduction by Stages. Lecture Notes in Mathematics, volume 1913, Springer Verlag. xvi+524 pp. ISBN: 978-3-540-72469-8.
Ortega, J.-P. and Ratiu, T. S. [2004] Momentum Maps and Hamiltonian Reduction. J.-P. Ortega and T.S. Ratiu. Progress in Mathematics, volume 222. Birkhäuser Boston, Inc., Boston, MA. xxxiv+497 pp. ISBN: 0-8176-4307-9. Award winning monograph of the Ferran Sunyer i Balaguer Prize.
Marsden, J.E., Ratiu, T.S., and Ortega, J.-P. (assistant author) [2003] Introduction to Mechanics and Symmetry, Solutions Manual. Springer-Verlag. 251 pp.
Ortega, J.-P. and Ratiu, T. S. [2004] Momentum Maps and Hamiltonian Reduction. J.-P. Ortega and T.S. Ratiu. Progress in Mathematics, volume 222. Birkhäuser Boston, Inc., Boston, MA. xxxiv+497 pp. ISBN: 0-8176-4307-9. Award winning monograph of the Ferran Sunyer i Balaguer Prize.
Marsden, J.E., Ratiu, T.S., and Ortega, J.-P. (assistant author) [2003] Introduction to Mechanics and Symmetry, Solutions Manual. Springer-Verlag. 251 pp.
Conference Papers
(Not applicable to NIE
staff as info will be
pulled from PRDS)
(Not applicable to NIE
staff as info will be
pulled from PRDS)
Ortega, J.-P. and Yin, D. [2023] Expressiveness and structure preservation in learning port-Hamiltonian systems. Proceedings of Geometric Science of Information 2023. Lecture Notes in Computer Science 14072, 313-322. Springer Verlag.
Da Costa, N., Mostajeran, C., and Ortega J.-P. [2023] The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle. Proceedings of Geometric Science of Information 2023. Lecture Notes in Computer Science 14071, 426-435. Springer Verlag.
Da Costa, N., Mostajeran, C., and Ortega J.-P. [2023] The Gaussian kernel on the circle and spaces that admit isometric embeddings of the circle. Proceedings of Geometric Science of Information 2023. Lecture Notes in Computer Science 14071, 426-435. Springer Verlag.
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