Research

Book Chapters

1. C. M. Alaíz, Á. Barbero and J. R. Dorronsoro. ‘Enforcing Group Structure through the Group Fused Lasso’. In: Artificial Neural Networks. Ed. by P. Koprinkova-Hristova, V. Mladenov and N. K. Kasabov. Vol. 4. Springer Series in Bio-/Neuroinformatics. Heidelberg, Germany: Springer-Verlag GmbH, Oct. 2015, pp. 349–371. isbn: 978-3-319-09902-6. doi: 10.1007/978-3-319-09903-3_17.

Journal Papers

1. M. Barroso, C. M. Alaíz, J. L. Torrecilla and Á. Fernández. ‘Functional diffusion maps’. In: Statistics and Computing 34 (Nov. 2023), p. 22. issn: 1368-9894. doi: 10.1007/s11222-023-10332-1.

2. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Adaptive Graph Laplacian MTL L1, L2 and LS-SVMs’. In: Logic Journal of the IGPL (2023). In press. issn: 1368-9894.

3. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Convex formulation for multi-task L1-, L2-, and LS-SVMs’. In: Neurocomputing 456 (June 2021), pp. 599–608. issn: 0925-2312. doi: 10.1016/j.neucom.2021.01.137.

4. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Multitask Support Vector Regression for Solar and Wind Energy Prediction’. In: Energies 13.23 (Nov. 2020), pp. 1930–1937. issn: 1996-1073. doi: 10.3390/en13236308.

5. A. Torres-Barrán, C. M. Alaíz and J. R. Dorronsoro. ‘Faster SVM training via conjugate SMO’. In: Pattern Recognition 111 (Sept. 2020), p. 107644. issn: 0031-3203. doi: /10.1016/j.patcog.2020.107644.

6. A. Catalina, C. M. Alaíz and J. R. Dorronsoro. ‘Combining Numerical Weather Predictions and Satellite Data for PV Energy Nowcasting’. In: IEEE Transactions on Sustainable Energy 11 (3 Oct. 2019), pp. 1930–1937. issn: 1949-3029. doi: 10.1109/TSTE.2019.2946621.

7. A. Catalina, A. Torres-Barrán, C. M. Alaíz and J. R. Dorronsoro. ‘Machine Learning Nowcasting of PV Energy using Satellite Data’. In: Neural Processing Letters (Jan. 2019). issn: 1573-773X. doi: 10.1007/s1 1063-018-09969-1.

8. C. M. Alaíz, M. Fanuel and J. A. K. Suykens. ‘Convex Formulation for Kernel PCA and its Use in Semisupervised Learning’. In: IEEE Transactions on Neural Networks and Learning Systems 29.8 (Aug. 2018), pp. 3863–3869. issn: 2162-237X. doi: 10.1109/TNNLS.2017.2709838.

9. C. M. Alaíz, M. Fanuel and J. A. K. Suykens. ‘Robust Classification of Graph-Based Data’. In: Data Mining and Knowledge Discovery 95 (1 Nov. 2018), pp. 230–251. issn: 1384-5810. doi: 10.1007/s10618-018-0603 -9.

10. C. M. Alaíz and J. A. K. Suykens. ‘Modified Frank–Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers’. In: Neurocomputing 320 (Dec. 2018), pp. 47–59. issn: 0925-2312. doi: 10.1016 /j.neucom.2018.08.049.

11. M. Fanuel, C. M. Alaíz, Á. Fernández and J. A. K. Suykens. ‘Magnetic Eigenmaps for the Visualization of Directed Networks’. In: Applied and Computational Harmonic Analysis 44 (Jan. 2018), pp. 189–199. issn: 1063-5203. doi: 10.1016/j.acha.2017.01.004.

12. A. Torres-Barrán, C. M. Alaíz and J. R. Dorronsoro. ‘ν-SVM Solutions of Constrained Lasso and Elastic Net’. In: Neurocomputing 275.Supplement C (Jan. 2018), pp. 1921–1931. issn: 0925-2312. doi: 10.1016/j .neucom.2017.10.029.

13. M. Fanuel, C. M. Alaíz and J. A. K. Suykens. ‘Magnetic eigenmaps for community detection in directed networks’. In: Phys. Rev. E 95 (2 Feb. 2017), p. 022302. issn: 2470-0045. doi: 10.1103/PhysRevE.95.022302.

14. C. M. Alaíz, F. Dinuzzo and S. Sra. ‘Correlation matrix nearness and completion under observation uncertainty’. In: IMA Journal of Numerical Analysis 35.1 (Dec. 2015), pp. 325–340. issn: 0272-4979. doi: 10.1093/imanum/drt056.

15. H. Azegrouz, G. Karemore, T. Torres, C. M. Alaíz, A. M. Gonzalez, P. Nevado, Á. Salmerón, T. Pellinen, M. Á. del Pozo, J. R. Dorronsoro and M. C. Montoya. ‘Cell-Based Fuzzy Metrics Enhance High Content Screening (HCS) Assay Robustness’. In: Journal of Biomolecular Screening 18.10 (Dec. 2013), pp. 1270–1283. issn: 1087-0571. doi: 10.1177/1087057113501554.

Conference Papers: Rank A

1. A. Catalina, C. M. Alaíz and J. R. Dorronsoro. ‘Accelerated Block Coordinate Descent for Sparse Group Lasso’. In: The 2018 International Joint Conference on Neural Networks (IJCNN). IEEE Computational Intelligence Society. Washington, D.C., U.S.A.: IEEE Xplore, July 2018, pp. 1–8. isbn: 2161-4407. doi: 10.1109/IJCNN.2018.8489078.

2. A. Catalina, C. M. Alaíz and J. R. Dorronsoro. ‘Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features’. In: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. Vol. 11325. Lecture Notes in Computer Science. Heidelberg, Germany: Springer-Verlag GmbH, Nov. 2018, pp. 13–26. isbn: 978-3-030-04302-5. doi: 10.1007/978-3-030-04303-2_2.

3. C. M. Alaíz and J. R. Dorronsoro. ‘The Generalized Group Lasso’. In: The 2015 International Joint Conference on Neural Networks (IJCNN). IEEE Computational Intelligence Society. Washington, D.C., U.S.A.: IEEE Xplore, July 2015, pp. 1–8. isbn: 978-1-4799-1959-8. doi: 10.1109/IJCNN.2015.7280612.

4. Á. Fernández, C. M. Alaíz, A. M. González, J. Díaz and J. R. Dorronsoro. Local Anisotropic Diffusion Detection of Wind Ramps. Neural Information Processing Systems - NIPS 2013 Workshop: Machine Learning for Sustainability. 2013. url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtbHN1c3R3c3xneDo3MDU5MDMzNmYzNTlmY2Zm.

5. C. M. Alaíz, Á. Barbero and J. R. Dorronsoro. ‘Sparse Methods for Wind Energy Prediction’. In: The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE Computational Intelligence Society. Washington, D.C., U.S.A.: IEEE Xplore, June 2012, pp. 1–7. isbn: 978-1-4673-1489-3. doi: 10.1109/IJCN N.2012.6252843.

Conference Papers: Rank B

1. C. M. Alaíz, Á. Fernández and J. R. Dorronsoro. ‘Visualization of the Feature Space of Neural Networks’. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2020. i6doc.com, Oct. 2020, pp. 169–174. isbn: 978-2-87587-074-2. url: https://www.esann.org/sites/default/files/proceedings/2020/ES2020-130.pdf.

2. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Convex Graph Laplacian Multi-Task Learning SVM’. In: Artificial Neural Networks and Machine Learning - ICANN 2020. Ed. by I. Farkaš, P. Masulli and S. Wermter. Vol. 12397. Lecture Notes in Computer Science. ENNS. Cham, Switzerland: Springer International Publishing, Oct. 2020, pp. 142–154. isbn: 978-3-030-61616-8. doi: 10.1007/978-3-030-6161 6-8_12.

3. A. Catalina, C. M. Alaíz and J. R. Dorronsoro. ‘Revisiting FISTA for Lasso: Acceleration Strategies Over The Regularization Path’. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2018. i6doc.com, Apr. 2018, pp. 285–290. isbn: 978-287587047-6. url: https://www.esann.org/sites/default/files/proceedings/legacy/es2018-81.pdf.

4. C. M. Alaíz, Á. Fernández and J. R. Dorronsoro. ‘Diffusion Maps Parameters Selection Based on Neighbourhood Preservation’. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2015. i6doc.com, Apr. 2015, pp. 501–506. isbn: 978-287587014-8. url: https://www.esann.org/sites/default/files/proceedings/legacy/es2015-97.pdf.

5. C. M. Alaíz, A. Torres and J. R. Dorronsoro. ‘Solving Constrained Lasso and Elastic Net Using ν-SVMs’. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2015. i6doc.com, Apr. 2015, pp. 267–272. isbn: 978-287587014-8. url: https://www.esann.org/sites/default/files/proceedings/legacy/es2015-95.pdf.

6. C. M. Alaíz, Á. Barbero and J. R. Dorronsoro. ‘Group Fused Lasso’. In: Artificial Neural Networks and Machine Learning - ICANN 2013. Ed. by V. Mladenov, G. Palm, A. Villa, B. Apolloni, P. Koprinkova-Hristova and N. Kasabov. Vol. 8131. Lecture Notes in Computer Science. ENNS. Heidelberg, Germany: Springer-Verlag GmbH, Sept. 2013, pp. 66–73. isbn: 978-3-642-40727-7. doi: 10.1007/978-3-64 2-40728-4_9.

7. Á. Fernández, C. M. Alaíz, A. González, J. Díaz and J. R. Dorronsoro. ‘Diffusion Methods for Wind power Ramp Detection’. In: Advances in Computational Intelligence. Ed. by I. Rojas, G. Joya and J. Gabestany. Vol. 7902. Lecture Notes in Computer Science. UMA et al. Heidelberg, Germany: Springer-Verlag GmbH, June 2013, pp. 106–113. isbn: 978-3-642-38678-7. doi: 10.1007/978-3-642-38679-4_9.

8. C. M. Alaíz, A. Torres and J. R. Dorronsoro. ‘Sparse Linear Wind Farm Energy Forecast’. In: Artificial Neural Networks and Machine Learning - ICANN 2012. Ed. by A. Villa, W. Duch, P. Érdi, F. Masulli and G. Palm. Vol. 7553. Lecture Notes in Computer Science. ENNS. Heidelberg, Germany: Springer-Verlag GmbH, Sept. 2012, pp. 557–564. isbn: 978-3-642-33266-1. doi: 10.1007/978-3-642-33266-1_69.

9. Á. Fernández, C. M. Alaíz, A. González, J. Díaz and J. R. Dorronsoro. ‘Diffusion Maps and Local Models for Wind Power Prediction’. In: Artificial Neural Networks and Machine Learning - ICANN 2012. Ed. by A. Villa, W. Duch, P. Érdi, F. Masulli and G. Palm. Vol. 7553. Lecture Notes in Computer Science. ENNS. Heidelberg, Germany: Springer-Verlag GmbH, Sept. 2012, pp. 565–572. isbn: 978-3-642-33266-1. doi: 10.1007/978-3-642-33266-1_70.

Conference Papers: Others

1. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Structure Learning in Deep Multi-Task Models’. In: Hybrid Artificial Intelligent Systems - HAIS 2023. Vol. 1401. Lecture Notes in Computer Science. Cham, Switzerland: Springer Nature, Sept. 2023, pp. 269–280. isbn: 978-3-031-40725-3. doi: 10.1007/978-3-031-40725-3_23.

2. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Convex Multi-Task Learning with Neural Networks’. In: Hybrid Artificial Intelligent Systems - HAIS 2022. Vol. 13469. Lecture Notes in Computer Science. Cham, Switzerland: Springer Nature, Sept. 2022, pp. 223–235. isbn: 978-3-031-15471-3. doi: 10.1007/978-3-031-15471-3_20.

3. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘Adaptive Graph Laplacian for Convex Multi-Task Learning SVM’. In: Hybrid Artificial Intelligent Systems - HAIS 2021. Vol. 12886. Lecture Notes in Computer Science. Cham, Switzerland: Springer Nature, Sept. 2021, pp. 219–230. isbn: 978-3-030-86271-8. doi: 10.1 007/978-3-030-86271-8_19.

4. D. López, C. M. Alaíz and J. R. Dorronsoro. ‘Modified Grid Searches for Hyper-Parameter Optimization’. In: Hybrid Artificial Intelligent Systems - HAIS 2020. Vol. 12344. Lecture Notes in Computer Science. Cham, Switzerland: Springer Nature, Nov. 2020, pp. 221–232. isbn: 978-3-030-61705-9. doi: 10.1007/978- 3-030-61705-9_19.

5. C. Ruiz, C. M. Alaíz and J. R. Dorronsoro. ‘A Convex Formulation of SVM-Based Multi-task Learning’. In: Hybrid Artificial Intelligent Systems - HAIS 2019. Vol. 11734. Lecture Notes in Computer Science. Cham, Switzerland: Springer Nature, Aug. 2019, pp. 404–415. isbn: 978-3-030-29858-6. doi: 10.1007/978-3-030- 29859-3_35.

6. C. M. Alaíz, Á. Fernández, Y. Gala and J. R. Dorronsoro. ‘Kernel K-Means Low Rank Approximation for Spectral Clustering and Diffusion Maps’. In: Intelligent Data Engineering and Automated Learning - IDEAL 2014. Ed. by E. Corchado, J. A. Lozano, H. Quintián and H. Yin. Vol. 8669. Lecture Notes in Computer Science. ENNS. Heidelberg, Germany: Springer-Verlag GmbH, Sept. 2014, pp. 239–246. isbn: 978-3-319-10839-1. doi: 10.1007/978-3-319-10840-7_30.

7. C. M. Alaíz and J. R. Dorronsoro. ‘On the Learning of ESN Linear Readouts’. In: Advances in Artificial Intelligence. Ed. by J. A. Lozano, J. A. Gámez and M. J. A. Vol. 7023. Lecture Notes in Computer Science. AEPIA. Heidelberg, Germany: Springer-Verlag GmbH, Nov. 2011, pp. 124–133. isbn: 978-3-642-25273-0. doi: 10.1007/978-3-642-25274-7_13.

8. C. M. Alaíz, Á. Barbero, Á. Fernández and J. R. Dorronsoro. ‘High Wind and Energy Specific Models for Global Production Forecast’. In: European Wind Energy Conference, EWEC 2009, Marseille, France, March 16-19, 2009, On Line Proceedings. EWEA. Mar. 2009. url: http://proceedings.ewea.org/ewec2009/allfiles2/393_EWEC2009presentation.pdf.

Other Publications

1. C. M. Alaíz. ‘Proximal Methods for Structured Group Features and Correlation Matrix Nearness’. Under the supervision of José R. Dorronsoro. PhD thesis. Madrid, Spain: Escuela Politécnica Superior, Universidad Autónoma de Madrid, July 2014. url: https://repositorio.uam.es/bitstream/handle/10486/662737/alaiz_gudin_carlos_maria.pdf;sequence=1.

2. C. M. Alaíz. ‘Advanced Methods for Recurrent Neural Networks Design’. Under the supervision of José R. Dorronsoro. MA thesis. Madrid, Spain: Escuela Politécnica Superior, Universidad Autónoma de Madrid, Nov. 2010. url: https://repositorio.uam.es/handle/10486/10020.