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Lorenzo Andrea Rosasco

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Lorenzo Andrea Rosasco Lorenzo Andrea Rosasco 28 dicembre 2018 PERSONAL INFORMATION Citizenship: Italian and United States Contact addresses: Universita´ degli Studi di Genova, via Dodecaneso 35, Genova, Italy RESEARCH INTERESTS My main interest is machine learning. Learning is widely acknowledged to be key for understanding human and machine intelligence. A computational scheme able to acquire knowledge and decision ma- king skills cannot simply memorize data. Rather, it must be able to learn, that is to efficiently process and summarize the flood of available data coming from sensory systems. At its core, learning is an inference problem from complex, high dimensional, noisy data. I am interested into the principles that allow to learn from small as well as massive samples of data and into the computational schemes that implement these principles. I pursue these questions using probabilistic and analytical tools, within a multidisciplinary approach drawing concepts and techniques primarily from computer science but al- so from statistics, engineering and applied mathematics. Applications in computer vision and robotics motivate some of the computational learning schemes I study and provide a natural ground to test their properties. Keywords: Machine Learning, Optimization, Inverse problems and Regularization, High Dimensio- nal Statistics and Probability, Reproducing Kernel Hilbert Spaces. EDUCATION & EMPLOYMENT fall 2017 Visiting Professor at the Massachusetts Institute of Technology. 2016 – Associate professor, Dipartimento di Informatica, Bioingegneria, Robotica, Inge- gneria, Ingegneria dei Sistemi (DIBRIS), University of Genova. 2012 2016 Assistant professor (with tenure), Dipartimento di Informatica, Bioingegneria, Ro- botica, Ingegneria, Ingegneria dei Sistemi (DIBRIS), University of Genova. 2013 – External Collaborator at the Istituto Italiano di Tecnologia, coordinating the Labo- ratory for Computational and Statistical Learning, a joint lab between the Istituto Italiano di Tecnologia and the Massachusetts Institute of Technology. fall 2016 Visiting Professor at the Massachusetts Institute of Technology. 2016 Research Affiliate at the Massachusetts Institute of Technology. fall 2015 Visiting Professor at the Massachusetts Institute of Technology. 2015 Research Affiliate at the Massachusetts Institute of Technology. fall 2014 Visiting Professor at the Massachusetts Institute of Technology. 2014 Research Affiliate at the Massachusetts Institute of Technology. fall 2013 Visiting Professor at the Massachusetts Institute of Technology. 2011 2012 Research scientist at the Massachusetts Institute of Technology. 2010 2012 Team leader at the Istituto Italiano di Tecnologia, coordinating the Laboratory for Computational and Statistical Learning, a joint lab between the Istituto Italiano di Tecnologia and the Massachusetts Institute of Technology. 2010 – 2011 Visiting scientist at the Massachusetts Institute of Technology. 2007 – 2009 Post doctoral Fellow at the Center for Biological and Computational Learning, Mas- sachusetts Institute of Technology. 2003 – 2006 PhD student in the Computer Science Department (DISI) at The University of Ge- nova. Regularization Approaches to Learning Theory, Supervisors: Prof. Alessandro Verri, Dr. Ernesto De Vito. 2002 Consultant within the start-up SLAM - Statistical Learning Applied to Market. De- sign and implementation of algorithms for the analysis and modelization of finan- cial data using machine learning. 1996 – 2001 Laurea (M.Sc. equivalent) degree in Physics, University of Genova (I), December 12, 2001. Dissertation title: ”Optimal Choice of Regularization Parameter in Statistical Lear- ning Theory”. Supervisors: Prof. Alessandro Verri, Computer Science Department (DISI), University of Genova; Michele Piana, Department of Mathematics (DIMA), University of Genova. SHORT VISITING POSITIONS 2011 visiting scientist at the Ecole´ Polytechnique in Paris, France (working with Stephane Mallat and Christope Giraud). 2009-2011 regularly visiting Steve Smale at City University of Hong Kong. 2008 regularly visiting Steve Smale at Toyota Technological Institute at Chicago. 2005 oct – nov visiting student at the Radon Institute for Computational and Applied Mathematics (working with Sergei Pereverzev). 2005 mar – jun visiting student at Toyota Technological Institute at Chicago, within the Learning Theory Program (working with Steve Smale). 2005 jan – jun visiting student working with Tomaso Poggio at the Center for Biological and Com- putational Learning, Massachusetts Institute of Technology. GRANTS and AWARDS • ERC consolidator grant e2 M (2019-2023). • AFOSR grant Principal Investigator, e170 K. • AXPO joint lab, e150 K (2019-2021). • SIMULA joint lab, e350 K (2018-2022). • Research and Innovation Staff Exchange (RISE), Unit coordinator, e40,5K. • AFOSR grant Principal Investigator, e170 K. • FIRB- Futuro in Ricerca project. Co-Principal Investigator, e307,187 K. • SEED project Dipartimento di Informatica, Bioingegneria, Robotica, Ingegneria, Ingegneria dei Sistemi (DIBRIS), e15 K. • Principal Investigator at the Center for Brain Minds and Machines, NSF funded $25 M. • Recepient of the Consorzio Italia-MIT 2005 Fellowship meant to bring top doctoral students from Italian member institutions to MIT, e5. • Recepient of the 2005 Silvio Tronchetti Provera Fellowship in the Information and Communica- tion Technology (ICT) area, e5 K. PUBLICATIONS Submitted 1. Matet, S., Rosasco, L., Villa, S., and Vu, B. L. Don’t relax: early stopping for convex regularization, arXiv preprint arXiv:1707.05422. 2. Salzo, S., Suykens, J. A., and Rosasco, L. Solving `p-norm regularization with tensor kernels, arXiv preprint arXiv:1707.05609. 3. Lin, J., and Rosasco, L. Generalization Properties of Doubly Online Learning Algorithms, arXiv preprint arXiv:1707.00577. 4. Garrigos, G., Rosasco, L., and Villa, S. Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry, arXiv preprint arXiv:1703.09477. 5. Garrigos, G., Rosasco, L. and Villa S. Iterative regularization via dual diagonal descent, arXiv preprint arXiv:1610.02170. 6. T Poggio, H Mhaskar, L Rosasco, B Miranda, Q Liao Why and When Can Deep–but Not Shallow– Networks Avoid the Curse of Dimensionality: a Review arXiv preprint arXiv:1611.00740 7. Lin, J., Rosasco, L., Villa, S. and Zhou, D.X Modified Fejer sequences and applications arXiv:1510.04641. 8. Pasquale, G. Ciliberto, C. Odone, F. Rosasco, L. and Natale, L. Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?, arXiv:1504.03154, submitted. 9. Rosasco, L., Villa, S., and Vu, B.C. Stochastic inertial primal-dual algorithms, arXiv: arXiv:1507.00852. 10. Rosasco, L., Villa, S., and Vu, B.C. A Stochastic forward-backward splitting method for solving monotone inclusions in Hilbert spaces, arxiv:1403.7999v1 11. Rosasco, L., Villa, S., and Vu, B.C. Convergence of Stochastic Proximal Gradient Algorithm. arXiv:1403.5074 Journal Papers 1. Rosasco, L., Villa, S., and u,˜ B. C. Stochastic Forward–Backward Splitting for Monotone Inclusions, Journal of Optimization Theory and Applications, 169(2), 388-406. 2. Rosasco, L., Villa, S. and Vu,˜ B., C. A first-order stochastic primal-dual algorithm with correction step, Numerical Functional Analysis and Optimization 38 (5), 602-626 3. Anselmi, F., Rosasco, L. and Poggio, T. On Invariance and Selectivity in Representation Learning Information and Inference 5 (2), 134-158, arXiv:1503.05938. 4. Rosasco, L., Villa, S., and Vu, B.C. A stochastic inertial forward-backward splitting algorithm for multi- variate monotone inclusions, Optimization 65 (6), 1293-1314, also arXiv:1507.00848. 5. Lin, J., Rosasco, L., and Zhou, D.X. Iterative Regularization for Learning with Convex Loss Functions, Journal of Machine Learning Research, arXiv:1403.5074. 6. Little, A. V., Maggioni, M., and Rosasco, L. Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature. Applied and Computational Harmonic Analysis, 43(3), 504-567. 7. Breschi, G., Ciliberto, C., Nieus, T., Rosasco, L., Taverna, S., Chiappalone, M., and Pasquale, V. Characterizing the input-output function of the olfactory-limbic pathway in the guinea pig, to appear in Computational Intelligence and Neuroscience. 8. Anselmi, F., Leibo, J., Rosasco, L. Mutch, J., Tacchetti, A. and Poggio, T. Unsupervised Learning of Invariant Representations , to appear in Theoretical Computer Science, also arXiv:1311.4158 9. Villa, S., L. Rosasco, L., Mosci, S. and Verri, A. Proximal methods for the latent group lasso penalty, Journal Computational Optimization and Applications archive Volume 58 Issue 2, 38-407 (Arxiv 1209.0368). 10. De Vito, E., Rosasco, L., and Toigo, A. Learning Sets with Separating Kernels, Applied and Compu- tational Harmonic Analysis, 37 185-217, 2014 (Arxiv 1204.3573 ). 11. Tacchetti, A. , Mallapragada, P., Santoro, M. and Rosasco, L., GURLS: a Least Squares Library for Supervised Learning, Journal of Machine Learning Research 14(1): 3201-3205 (2013). 12. Mosci, S., Rosasco, L., Santoro, M., Verri, A. and Villa, S., Nonparametric Sparsity and Regularization., Journal of Machine Learning Research 14(1): 1665-1714 (2013). 13. Alvarez, M., Lawrence, N. and Rosasco, L., Kernels for Vector-Valued Functions: a Review., Founda- tions and Trends in Machine Learning 4(3):195-266, 2012, (also MIT-CSAIL-TR-2011-033/CBCL- 301). 14. Baldassarre, L., Barla, A., Rosasco, L. and Verri, A., Multi-Output Learning via Spectral Filtering, Machine Learning 87(3): 259-301
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