Academic Profile : Faculty

photo_NTU_1_2.png picture
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)
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.
Books
(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.
Conference Papers
(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.
 
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