Short CV in PDF - Max-Planck-Gesellschaft

Francesco Dinuzzo, Ph.D. 4
Publications
Articles in peer-reviewed journals
1. C. A. Gudín, F. Dinuzzo, S. Sra. Correlation matrix nearness and completion under observation
uncertainty, IMA Journal of Numerical Analysis, (in press) 2013.
2. S. Del Favero, D. Varagnolo, F. Dinuzzo, L. Schenato, and G. Pillonetto. Finding potential support
vectors in separable classification problems. IEEE Transactions on Neural Networks and Learning
Systems, 24(11):1799-1813, 2013.
3. F. Dinuzzo. Learning output kernels for multi-task problems, Neurocomputing, 118:119-126, 2013.
4. F. Dinuzzo. Analysis of fixed-point and coordinate descent algorithms for regularized kernel methods.
IEEE Transactions on Neural Networks, 22(10), 2011.
5. F. Dinuzzo, G. Pillonetto, and G. De Nicolao. Client-server multi-task learning from distributed
datasets. IEEE Transactions on Neural Networks, 22(2):290-303, 2011.
6. F. Dinuzzo. Learning functional dependencies with kernel methods. Scientifica Acta, 4(1):MS 16-25,
2010.
7. G. Pillonetto, F. Dinuzzo, and G. De Nicolao. Bayesian online multi-task learning of Gaussian
processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2):193-205, 2010.
8. F. Dinuzzo and A. Ferrara. Higher order sliding modes controllers with optimal reaching. IEEE
Transactions on Automatic Control 54(9):2126-2136, 2009.
9. F. Dinuzzo and A. Ferrara. Finite-time output stabilization with second order sliding modes. Automatica,
45(9): 2169-2171, 2009.
10. F. Dinuzzo and G. De Nicolao. An algebraic characterization of the optimum of regularized kernel
methods. Machine Learning, 74(3): 315-345, 2009.
11. F. Dinuzzo, M. Neve, G. De Nicolao, and U. P. Gianazza. On the representer theorem and equivalent
degrees of freedom of SVR. Journal of Machine Learning Research, 8: 2467-2495, 2007.
Refereed international conference publications
1. F. Dinuzzo and B. Schölkopf. The representer theorem for Hilbert spaces: a necessary and sufficient
condition. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, Lake Tahoe
NV (USA), 3-6 December 2012.
2. K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Schölkopf. Learning from Distributions via Support
Measure Machines. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, Lake
Tahoe NV (USA), 3-6 December 2012.
3. C. Persello, and F. Dinuzzo. Interactive domain adaptation technique for the classification of remote
sensing images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium,
Munich, Germany, July 2012.
4. S. Del Favero, D. Varagnolo, F. Dinuzzo, L. Schenato, and G. Pillonetto. On the discardability of data
in support vector classification problems. In Proceedings of the IEEE Conference on Decision and Control
and European Control Conference, Orlando, FL (USA), December 2011.