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 also 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 (2012). 15. Mosci, S., Rosasco, L., Verri, A. and Villa, S., Applications of Variational Convergence to Regularized Learning Algorithms, Optimization 61(3):287-305, 2012.. 16. De Vito, E., Pereverzev, S. and Rosasco, L., Adaptive Learning via the Balancing Principle, Founda- tions of Computational Mathematics, 8 355-479 (2010). 17. P. Fardin, A. Barla, S. Mosci, L. Rosasco, A. Verri, R. Versteeg, H. Caron, J. Molenaar, I. Ora, A. Eva, M. Puppo and L. Varesio, A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients, Molecular Cancer 2010, 9:185. 18. Fardin, P., Barla A., Mosci, S., Rosasco, L., Verri, A. and Varesio, L., Identification of multiple hypoxia signatures in neuroblastoma cell lines by l1-l2 regularization and data reduction, Journal of Biomedicine and Biotechnology, Volume 2010 (2010) 19. Fardin, P., Barla A., Mosci, S., Rosasco, L., Verri, A. and Varesio, L., The l1-l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines, BMC Genomics 2009, 10:474. 20. Del Bono, V., Mularoni, A., Furfaro, E., Delfino, E., Rosasco, L., Miletich, F., and Viscoli, C., Clinical Evaluation of a (1,3)-beta-D-Glucan Assay for Presumptive Diagnosis of Pneumocystis jiroveci Pneumonia in Immunocompromised Patients, Clin. Vaccine Immunol. 2009 16: 1524-1526. 21. Rosasco, L., Belkin, M. and De Vito, E., On Learning with Integral Operators, Journal of Machine Learning Research, 11(Feb):905?934, 2010. 22. Smale, S., Rosasco, L., Bouvrie, J., Caponnetto, A. and Poggio, T., The Mathematics of the Neural Response, Foundations of Computational Mathematics, June 2009, DOI 10.1007/s10208-009-9049- 1. 23. De Mol, C., De Vito, E. and Rosasco, L., Elastic Net Regularization in Learning Theory, Journal of Complexity, Volume 25 , Issue 2 (April 2009), Pages 201-230, 2009. 24. Lo Gerfo, L., Rosasco, L., Odone, F., De Vito E. and Verri, A., Spectral Algorithms for Supervised Learning, Neural Comp..2008; 20: 1873-1897. 25. Bauer, F., Pereverzev, S. and Rosasco, L., On Regularization Algorithms in Learning Theory, J. Com- plexity 23(1): 52-72 (2007). 26. Yao, Y., Caponnetto, A. and Rosasco, L., Early Stopping for Gradient Descent Boosting, Constr. Approx. 26 (2007), no. 2, 289–315. 27. De Vito, E., Rosasco, L. and Caponnetto, A., Discretization Error Analysis for Tikhonov Regularization, Analysis and Applications Vol. 4, No. 1 (January 2006). 28. De Vito, E., Rosasco, L., Caponnetto, A., De giovannini, U. and Odone, F., Learning as an Inverse Problem, Journal of Machine Learning Research 6(May):883–904, 2005. 29. De Vito, E., Caponnetto, A. and Rosasco, L., Model Selection for Regularized Least-Squares Algorithm in Learning Theory, Foundations of Computational Mathematics Volume 5, Number 1 pp. 59 - 85, February 2005. 30. Rosasco, L., De Vito, E., Caponnetto, A., Piana, M. and Verri, A., Are Loss Function All the Same?, Neural Computation, Vol 16, Issue 5, 2004. 31. De Vito, E., Rosasco, L., Caponnetto, A., Piana, M. and Verri, A., Some Properties of Regularized Kernel Methods, Journal of Machine Learning Research 5(Oct):1363–1390, 2004.

Chapters in Books 1. Mutch, J., Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T. Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex, Computational and Cognitive Neuroscience of Vision (pp. 85-104). Springer Singapore. 2. Villa, S., Rosasco, L. and Poggio, T. On Learnability, Complexity and Stability, ”Empirical Inference, Festschrift in Honor of Vladimir N. Vapnik”. Editors: Scholkopf,¨ Bernhard; Luo, Zhiyuan; Vovk, Vladimir. Springer-Verlag Berlin and Heidelberg GmbH, Chapter 7, page 59-70, 2013. Also Arxiv 1303.5976 3. G. Chen, A.V. Little, M. Maggioni, L. Rosasco, Some recent advances in multiscale geometric analysis of point clouds, in Wavelets and Multiscale Analysis: Theory and Applications (March, 2010), Springer.

Conference and Workshop Papers 1. Rudi, A., Camoriano, R., and Rosasco, L. Generalization properties of learning with random features, To appear in NIPS 2017. 2. Rudi, A., Carratino, L., and Rosasco, L. FALKON: An Optimal Large Scale Kernel Method, To appear in NIPS 2017. 3. Ciliberto, C., Rudi, A., Rosasco, L., and Pontil, M. Consistent Multitask Learning with Nonlinear Output Relations, To appear in NIPS 2017. 4. Camoriano, R., Pasquale, G., Ciliberto, C., Natale, L., Rosasco, L., and Metta, G. Incremental robot learning of new objects with fixed update time, In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 3207-3214). IEEE. 5. Anselmi, F., Evangelopoulos, G., Rosasco, L., and Poggio, T. Symmetry Regularization, Center for Brains, Minds and Machines (CBMM). 6. Higy, B., Ciliberto, C., Rosasco, L., and Natale, L. Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics, In Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on (pp. 117-124). IEEE. 7. Jamali, N., Ciliberto, C., Rosasco, L., and Natale, L. Active perception: Building objects’ models using tactile exploration, In Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on (pp. 179-185). IEEE. 8. Pasquale, G., Ciliberto, C., Rosasco, L., and Natale, L. Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network, Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on (pp. 4904-4911). IEEE. 9. Lin, J., Camoriano, R., and Rosasco, L. Generalization properties and implicit regularization for multiple passes SGM, International Conference on Machine Learning (pp. 2340-2348). 10. Lin, J., and Rosasco, L. Optimal Learning for Multi-pass Stochastic Gradient Methods, Advances in Neural Information Processing Systems (pp. 4556-4564). 11. Ciliberto, C., Rudi, A., and Rosasco, L. A Consistent Regularization Approach for Structured Predic- tion, Advances in Neural Information Processing Systems (pp. 4412-4420). 12. Camoriano, R., Traversaro, S., Rosasco, L., Metta, G., and Nori, F. Incremental Semiparametric Inverse Dynamics Learning, IROS 2016, arXiv:1601.04549. 13. Angles, T. Camoriano, R., Rudi, A. and Rosasco, L. NYTRO: When Subsampling Meets Early Stopping AISTATS 2016. 14. Poggio, T., Rosasco, L., Shashua, A., Cohen, N., and Anselmi, F. Notes on hierarchical splines, dclns and i-theory, Center for Brains, Minds and Machines (CBMM). 15. Nickel, M., Rosasco, L. and Poggio, T. Holographic Embeddings Knowledge Graphs, AAAI-16, also arXiv:1510.04935. 16. Poggio, T., Anselmi, F., and Rosasco, L. I-theory on depth vs width: hierarchical function composition, Center for Brains, Minds and Machines (CBMM). 17. Rudi, A., Camoriano, R. and Rosasco, L. Less is More: NyströmComputational Regularization, accepted to NIPS 2015 (Oral presentation, < 1% acceptance), arXiv:1507.04717. 18. Rosasco, L. and Villa, S. Learning with incremental iterative regularization, accepted to NIPS 2015, arXiv:1405.0042. 19. Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L. Teaching iCub to recognize objects using deep Convolutional Neural Networks, In Machine Learning for Interactive Systems (pp. 21-25). 20. Badino, L., Mereta, A. and Rosasco, L. Discovering discrete subword units with Binarized Autoencoders and Hidden-Markov-Model Encoders, accepted to Interspeech 2015. 21. Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. and Poggio, T. Discriminative Template Lear- ning in Group-Convolutional Networks for Invariant Speech Representations, accepted to Interspeech 2015. 22. Ciliberto, C., Villa, S. and Rosasco, L., Learning Multiple Visual Tasks while Discovering their Structu- re, arXiv:1504.03106, accepted to CVPR 2015. 23. Ciliberto, C., Mroueh, Y., Poggio, T. and Rosasco, L., Convex Learning of Multiple Tasks and their Structure, arXiv:1504.03101 accepted ICML 2015. 24. Mroueh, Y. and Rosasco, L. On efficiency and low sample complexity in phase retrieval, IEEE Interna- tional Symposium on Information Theory (ISIT) 2014: 931-935, 2014. 25. Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. and Poggio, T. A Deep Representation for Invariance and Music Classification, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. 26. Zhang, C., Voinea, S., Evangelopoulos, G., V Rosasco, L. and Poggio, T. Phone Classification by a Hierarchy of Invariant Representation Layers INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association. 27. Voinea, S., Zhang, C., Evangelopoulos, G., V Rosasco, L. and Poggio, T. Word-Level Invariant Repre- sentations from Acoustic Waveforms INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association. 28. Ciliberto, C., Fiorio, L., Maggiali, M. Natale, L., Rosasco, L., Metta, G., Sandini, G. and Nori, F. Ex- ploiting Global Force Torque Measurements for Local Compliance Estimation in Tactile Arrays IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014 29. Fanello, S., Ciliberto, C., Santoro, M., Natale, L., Metta, G., Rosasco, L. and Odone, F. iCub World: Friendly Robots Help Building Good Vision Data-Sets IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2013. 30. Rudi, A., Canas, G., and Rosasco, L. On the Sample Complexity of Subspace Learning In Advances in Neural Information Processing Systems (NIPS) 26. 2013. 31. Ciliberto, C., Fanello, S.. Santoro, M., Natale, L., Metta, G. and Rosasco, L. On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013. 32. Mroueh, Y., Rosasco, L. Q-ary Compressive Sensing. Proceedings SampTA, 2013, also Arxiv 1302.5168. 33. Mroueh, Y., Poggio, T., Rosasco, L. Slotine, J.J. Multi-class Learning with Simplex Coding. In Advan- ces in Neural Information Processing Systems, NIPS 2012.. 34. Rosasco, L., Villa, S., Mosci, S., Santoro, M., and Verri, A., Is there sparsity beyond additive models?, Proceedings of IFAC System Identification 16, 2012. 35. Canas, G.D., Rosasco, L., Poggio, T. Learning Manifolds with K-Means and K-Flats. In Advances in Neural Information Processing Systems, NIPS 2012. 36. Canas, G.D., Rosasco, L. Learning Probability Measures with respect to Optimal Transport Metrics.. In Advances in Neural Information Processing Systems, NIPS 2012. 37. A.V. Little, M. Maggioni, L. Rosasco, Multiscale Geometric Methods for Estimating Intrinsic Dimen- sion, Proc. SampTA 2011 (2010). 38. De Vito, E., Rosasco, L. and Toigo, A. , Support Estimation with Regularization, Advances in Neural Information Processing Systems Proc. SampTA 2011 (2010). 39. De Vito, E., Rosasco, L. and Toigo, A. , Spectral Regularization for Support Estimation, Advances in Neural Information Processing Systems (NIPS) 23, 2010. 40. Mosci, S., Villa, S. and Rosasco, L., A primal-dual algorithm for group sparse regularization with overlapping groups. Advances in Neural Information Processing Systems (NIPS) 23, 2010. 41. Baldassarre, L., Barla, A., Rosasco, L. and Verri, A., Learning Vector Fields via Spectral Filtering. In proceeding of ECML 2010. 42. Mosci, S., Rosasco, L., Santoro, M., Verri, A. and Villa, S., Solving Structured Sparsity Regularization with Proximal Methods. In proceeding of ECML 2010. 43. Rosasco, L., Mosci, S., Santoro, M., Verri, A. and Villa, S., A Regularization Approach to non linear Variable Selection. AISTAT 2010. 44. Bouvrie, J., Rosasco, L. and Poggio, T. , ”On Invariance in Hierarchical Models, Advances in Neural Information Processing Systems (NIPS) 22, 2009. 45. N. Noceti, B. Caputo, C. Castellini, L. Baldassarre, A. Barla, L. Rosasco, F. Odone and G. Sandini Towards a theoretical framework for learning multi-modal patterns for embodied agents, ICIAP-09– 15th, International Conference on Image Analysis and Processing. 46. Rosasco, L., Belkin, M., and De Vito, E., A Note on Learning with Integral Operators, COLT 2009– 22nd Annual Conference on Learning Theory. 47. Barla, A., Mosci, S., Rosasco, L. and Verri, A., Finding structured gene signatures, IEEE Proc. of Work- shop on Data Mining in Functional Genomics (IEEE International Conference on Bioinformatics and Biomedicine), Nov 2008. 48. Barla, A., Mosci, S., Rosasco, L. and Verri, A., A method for robust variable selection with significance assessments 16th European Symposium on Artificial Neural Networks. 49. Mosci, S., Rosasco, L. and Verri A., Dimensionality reduction and generalization , ACM International Conference Proceeding Series; Vol. 227 archive Proceedings of the 24th International Conference on Machine Learning (ICML). 50. Caponnetto, A., Rosasco, L., Odone, F. and Verri, A., Support Vectors Algorithms as Regularization Networks, 13th European Symposium on Artificial Neural Networks (ESANN). 51. Rosasco, L., Caponnetto, A., De Vito, E., De Giovannini, U. and Odone, F., Learning, Regularization and Ill-posed Inverse problems, Eighteenth Annual Conference on Neural Information Processing Systems (NIPS). Technical Reports

1. Pasquale, G. Mar, T., Ciliberto, C. Rosasco, L. and Natale, L. Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives arxiv:1509.06939 2. Mroueh, Y., and Rosasco, L. Quantization and Greed are Good: One bit Phase Retrieval, Robustness and Greedy Reﬁnements. arXiv:1312.1830 3. Poggio, T., S. Voinea and L. Rosasco, Online Learning, Stability, and Stochastic Gradient Descent, Cornell University Library, arXiv:1105.4701v2 [cs.LG], May 25, 2011 4. Leibo, J.Z., J. Mutch, L. Rosasco, S. Ullman, and T. Poggio, Learning Generic Invariances in Object Recognition: Translation and Scale. MIT-CSAIL-TR-2010-061/CBCL-294, Massachusetts Institute of Technology, Cambridge, MA, December 30, 2010. 5. Mutch, J., J.Z. Leibo, S. Smale, L. Rosasco, and T. Poggio, Neurons That Confuse Mirror-Symmetric Object Views. MIT-CSAIL-TR-2010-062/CBCL-295, Massachusetts Institute of Technology, Cam- bridge, MA, December 31, 2010. 6. Wibisono, A., J. Bouvrie, L. Rosasco, and T. Poggio, Learning and Invariance in a Family of Hie- rarchical Kernels. MIT-CSAIL-TR-2010-035 / CBCL-290, Massachusetts Institute of Technology, Cambridge, MA, July 30, 2010 7. Bouvrie, J., Rosasco, L. , Shakhnarovich, G. and Smale, S., hi the Shannon Entropy of the Neural Response. CBCL-281, MIT-CSAIL-TR-2009-049, Massachusetts Institute of Technology, Cambridge, MA, October 9, 2009. 8. Rosasco, L., Mosci, S., Santoro, M., Verri, A. and Villa, S., Iterative Projection Methods for Structured Sparsity Regularization, MIT-CSAIL-TR-2009-50 / CBCL-282, Massachusetts Institute of Technolo- gy, Cambridge, MA, October 14, 2009. Submitted. 9. Wibisono, A., Rosasco, L., and Poggio, T., Sufﬁcient Conditions for Uniform Stability of Regularization Algorithms, CBCL paper #284/CSAIL Technical Report#MIT-CSAIL-TR-2009-060, Massachusetts Institute of Technology, Cambridge, MA, December 1, 2009. 10. Rosasco, L., De Vito, E. and Verri, A., Spectral Regularization Algorithms for Learning Technical report DISI-TR-05-18. 11. Caponnetto, A., Rosasco, L., De Vito, E. and Verri, A., Empirical Effective Dimension and Optimal Rates for Regularized Least Squares, CBCL Paper 252/AI Memo 2005-019, MIT, Cambridge, MA, May 2005. 12. Caponnetto, A. and Rosasco, L., Non Standard Support Vector Machines and Regularization Networks, Technical Report DISI, DISI-TR-04-03. 13. De Vito, E., Rosasco, L., Caponnetto, A., Piana, M. and Verri, A., Representer Theorem for Convex Loss Fuctions, Technical Report DISI, DISI-TR-03-13. 14. De Vito, E., Rosasco, L., Caponnetto, A., Piana, M. and Verri, A., Minimization of Tikhonov Functio- nals: the Continuos Setting, Technical Report DISI, DISI-TR-03-14. Technical Report DISI-TR-02-09, Dipartimento di Informatica e Scienze dell’Informazione (DISI), University of Genova.

TEACHING

The latest evaluation of my teaching at MIT reported an overall rating of 6.4 out of 7 (1=Very Poor, 7=Excellent), and in particular: Stimulated interest=6.5, Displayed thorough knowledge of subject ma- terial=6.8, Helped me learn=6.5.

• Spring 2018: Probability, Information Theory and Inference, undergraduate course, University of Genova. • Spring 2017. Machine Learning Crash Course, PhD and Master Level course, Scuola Galileana, University of Padova. • Summer 2017. Machine Learning Crash Course, PhD and Master Level course, University of Geno- va. • Spring 2017: Regularization Methods for Machine Learning, PhD Course, SIMULA, Oslo. • Spring 2017: Probability, Information Theory and Inference, undergraduate course, University of Genova. • Fall 2016. Machine Learning , Master Level course, University of Genova. • Fall 2016. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Pog- gio). • Summer 2016: Regularization Methods for Machine Learning, PhD Course, University of Genova. • Spring 2016. Intelligence Systems and Machine Learning Module 2: Machine Learning, Master Level course, University of Genova. • Fall 2015. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Pog- gio). • Summer 2015. Machine Learning Summer School (MLSS) Kyoto.Tutorial on learning data representation. • Summer 2015. MBL-Woodshole summer school: Brains, mind and machine. • Summer 2015. Machine Learning Crash Course, PhD and Master Level course, University of Genova (with Francesca Odone). • Spring 2015. Intelligence Systems and Machine Learning Module 2: Machine Learning, Master Level course, University of Genova. • Fall 2014. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Pog- gio). • Summer 2014: Regularization Methods for Machine Learning, PhD Course, University of Genova (with Francesca Odone). • Summer 2014. Summer Course at MLB, Woods Hole: Brains, Minds and Machines, PhD Course, MIT. • Spring 2014. Intelligence Systems and Machine Learning Module 2: Machine Learning, Master Level course, University of Genova. • Spring 2014. Machine Learning Crash Course, PhD and Master Level course, University of Genova (with Francesca Odone). • Fall 2013. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Pog- gio). • Summer 2013: Regularization Methods for Learning High Dimensional Data, PhD Course, University of Genova (with Francesca Odone). • July 2012: National Graduate Student School SIDRA, Sistemi Stocastici: stima ed identiﬁcazione, Bertinoro, Italy. • Bertinoro international Spring School (BISS), 12-16 March 2012, PhD course, (with Francesca Odone). • Spring 2012. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Fall 2011. Guest Lectures for the course What’s Intelligence?, PhD course, MIT. • Summer 2011: Regularization Methods for Learning High Dimensional Data, PhD course, University of Genova (with Francesca Odone). • Spring 2011. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Summer 2010: Regularization Methods for Learning High Dimensional Data, PhD course, University of Genova (with Francesca Odone). • Spring 2010. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Guest Lectures for the course 6.873/HST.951: Medical Decision Support, PhD course, MIT (lectured by R. C. Lacson, S. A. Vinterbo). • Summer 2009: Regularization Methods for Learning High Dimensional Data, PhD course, University of Genova (with Francesca Odone). • Spring 2009. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Winter 2009. IAP Course: From Understanding Cortex to Building Intelligent Machines, (with Tomaso Poggio and Thomas Serre). • Summer 2008. Regularization Approaches to Learning, PhD course, University of Genova (with Francesca Odone). • Spring 2008. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Winter 2008. IAP Course: From Understanding Cortex to Building Intelligent Machines, (with Tomaso Poggio and Thomas Serre). • Fall 2007: Statistical Learning, Master Course, Computer Science Department (DISI), University of Genova (with Alessandro Verri). • Spring 2007. 9.520: Statistical Learning Theory and Applications, PhD course, MIT (with Tomaso Poggio). • Fall 2006: Statistical Learning, Master Course, Computer Science Department (DISI), University of Genova (with Alessandro Verri). • Spring 2006-2007. Guest Lectures for the course Mathematical foundations of Learning Department of Mathematics (DIMA), University of Genova (lectured by Ernesto De Vito). • 2005-2006, Guest Lectures for course Stochastic Process, Engineering Department of the University of Genova (lectured by Nino Zangh´ı ). • 2005-06: Statistical Learning, Master Course, DISI- University of Genova (with Alessandro Verri). • 2004-05: Statistical Learning, Guest Lectures Statistical Learning, Master Course, Computer Science Department (DISI), University of Genova (with Alessandro Verri). • 2003-04: Statistical Learning Master Course, Computer Science Department (DISI), University of Genova (with Alessandro Verri).

SERVICES AND OTHER PROFESSIONAL ACTIVITIES

Organization of scientiﬁc events

• Organizer, Large scale learning session, International Symposium on Mathematical Programming (ISMP 2018), Bordeaux • Organizer (with A. Stuart) Inverse Problems in Machine Learning workshop, Caltech, USA. • Organizer (with Sebastien Bubeck and Sasha Tsybakov) of the Workshop “Learning Theory’ at the FoCM conference 2017, Barcelona. • Organizer (with Silvia Villa, Roberto Lucchetti) of the Learning, Games and Optimization minisym- posium, SIMAI, Milan 2016. • Organizer (with Tomaso Poggio, Max Nickel, Pierre Baldi) of the Deep Learning workshop, MIT, Boston 2016 • Organizer (with Giorgio Metta, Boris Katz) of the Brain Minds & Machine workshop, Sestri Levante, Italy 2016. • Organizer (with Zoubin Ghaharamani, Thomas Hofmann, Neil Lawrence, Bernhard Scholkopf¨ ) of the DALI 2016 - Data Learning and Inference meeting, Sestri Levante, Italy 2016. • Organizer (with Nicolo Cesa-Bianchi) of the Learning Theory workshop within the DALI 2016 - Data Learning and Inference meeting, Sestri Levante, Italy 2016. • Organizer (with Matthias Hein, Gabor Lugosi) of the Dagstuhl Seminar Computational and Mathe- matical Foundation of Learning Theory, August 2015. • Organizer (with Tomaso Poggio) of the Workshop “Learning Theory’ at FoCM 2014, Montevideo. • Organizer (with Silvia Villa) of the Mini-symposium “The Mathematics of Learning from Data” Societa` Italiana di Matematica Applicata (SIMAI) 9th Congress, 2015 - Taormina, Italy. • Organizer (with Patrick Combettes, Saverio Salzo, Silvia Villa) of the Workshop “Optimization and Dynamical Processing for Machine Learning and Inverse Problems” at Fondazione Mediater- raneo, Sestri Levante Genova Italy, September 2015. • Organizer (with Ryan Adams, Sham Kakade, Stephanie Telex) of the Workshop “New England Machine Learning Day” at MSR, Boston, USA. • Organizer (with Tomaso Poggio) of the Workshop “Learning Data Representation: Hierarchies and Invariance” at MIT. • Organizer of the 2012 - Genova Machine Learning and Robotics Seminar Series. • Organizer of the 2012 Cambridge Machine Learning Colloquium and Seminar Series at MIT. • Organizer of the 2010-2012 Brain and Machines Seminar Series at MIT, sponsored by MIT-IIT colla- borative agreement. • Organizer (with Matthias Hein, Gabor Lugosi and Steve Smale) of the Dagstuhl Seminar Computa- tional and Mathematical Foundation of Learning Theory, July 2011. • Organizer (with Sergei Pereverzev) of the Workshop on Inverse Problems in Learning and Data Driven Model within the Special Semester on Computational and Applied Inverse Problems (to be held in Linz, Austria, July 2010). • Organizer (with Sergei Pereverzev) of the Workshop on Inverse Problems in Learning within the In- ternational Conference on Inverse Problems: Modeling and Simulation (to be held in Turkey, May 24 -29, 2010). • Organizer (with Mauricio Alverez and Neil Lawrence) of the workshop Kernels for Multiple Out- puts and Multi-task Learning: Frequentist and Bayesian Points of View, within Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS). • Organizer of the mini-symposium Learning High Dimensional Data, within the International Con- ference on Applied Inverse Problems 2009 (Vienna, Austria). • Organizer (with Andrea Caponnetto) of the workshop Learning from Examples as an Inverse Problem, within the International Conference on Applied Inverse Problems 2007. • Organizer (with Ernesto De Vito and Alessandro Verri) of the workshop Trends in Computational Science in 2006 (main speakers: Ingrid Daubachies, Steve Smale, Ron Devore, Tomaso Poggio). • Organizer (with Ernesto De Vito and Alessandro Verri) of the workshop Analytic Methods for Lear- ning Theory in 2006 (main speakers: Filippo De Mari, Massimiliano Pontil, Vladimir Temlyakov) • Organizer (with Ernesto De Vito and Alessandro Verri) of the workshop Analytic Methods for Lear- ning Theory in 2005 (main speakers: Steve Smale, Tomaso Poggio).

Academic service

2018 Member of PhD Committee of Magda Gregorova, University of Geneva, Switzer- tland. 2018 Member of PhD Committee of Charles Frogner, MIT, Boston, USA. 2018 Scientiﬁc Advisory Board, Data Science Initiative SIMULA, Norway 2017 Member of PhD Committee of Aymeric Dieulevet, ENS, Paris, France. 2017 Member of Habilitation a` Diriger la Recherche Committee of Maurizio Filippone, Eurecom, Nice. 2017 Member of PhD Committee of Chyuan Zhang, MIT, Boston, USA. 2017 PhD Committee, ’School of Computer Science’, Universita´ di Genova. 2013 -2016 PhD Committee, ’School of Bioengineering and robotics’, Universita´ di Genova. 2015, 2017 PhD selection committee,’School of Bioengineering and robotics’, Universita´ di Ge- nova. 2017 Scientiﬁc council of Maison des Sciences de l’Homme et de la Societe´ Sud Est ( Uni- versite´ de Nice, Universite´ di Corsica, CNRS).

Reviewer and editor service • Editorial Board for: Journal of Machine Learning Research, Statistics, Annals of Mathematics and Artiﬁcial Intelligence. • Journal Reviewer for Journal of Machine Learning Research, Machine Learning Journal, Neural Computation, IEEE Transaction on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intel- ligence, IEEE Transactions on Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, Foundations of Computational Mathematics, Journal of Complexity, Constructive Ap- proximation, Applied and Computational Harmonic Analysis, Journal of Statistical Planning and Inference. • Area Chair for Neural Information Processing System (NIPS) 2013-2016-2017-2018, International Conference on Machine Learning (ICML) 2015, International Conference on Computer Vision (ICCV) 2017. • Programme Committee for Conference on Computational Learning Theory (COLT) 2011, 2012, 2013, 2014, 2015, 2018. • Conference Reviewer for Conference on Neural Information Processing System (NIPS), Conference on Computational Lear- ning Theory (COLT), International Conference on Machine Learning (ICML), International Con- ference on Machine Learning (ECML), International Conference on Artiﬁcial Intelligence and Sta- tistics (AISTATS).

Invited Talks & Lectures

December 2018 CMStatistics, Pisa November 2018 Horizon de math, ENS, Paris October 2018 ORFE, Princeton July 2018 International Symposium on Mathematical Programming (ISMP 2018), Bordeaux June 2018 International Society for Nonparametric Statistics, Salerno June 2018 Alan Turing Institute, London June 2018 ‘From optimization to regularization in inverse problems and machine learning’ , SIAM Conference on Imaging Science, Bologna June 2018 ’Low dimensional structure in imaging science’ , SIAM Conference on Imaging Science, Bologna Mar 2018 Universita´ di Pavia Mar 2018 Deep learning & Inverse Problems workshop, Oberwolfach Feb 2018 Machine Learning & Inverse Problems workshop, Caltech Feb 2018 UCLA Jan 2018 Theoretical and algorithmic underpinnings of Big Data workshop, Newton Institute Cam- bridge Jan 2018 Google DeepMind Jan 2018 Amazon Cambridge UK Nov 2017 Massachusetts Institute of Technology Oct 2017 Ohio State University, Columbus Oct 2017 Air Force Technological Institute, Dayton Oct 2017 New York University, New York June 2017 Institut Henri Poincare´ 2017, ’Structured Regularization for High-Dimensional Data Ana- lysis’, Paris June 2017 CERN 2017, ’Data Science Seminar Series’ , Geneva June 2017 Applied Inverse Problems conference 2017, ’Modern regularization techniques in data- based learning’, Hangzhou June 2017 Applied Inverse Problems conference 2017, ’Multi-penalty regularization and applications in high dimensional data learning’, Hangzhou June 2017 Applied Inverse Problems conference 2017, ’Deep Neural Networks: Theory and Applica- tions’, Hangzhou June 2017 International conference on applied and Computational Harmonic Analysis 2017, Shan- ghai May 2017 SIOPT 2017, ’Robustness and Dynamics in Optimization’ , Vancouver May 2017 SIOPT 2017, ’First-Order Methods and Applications’ , Vancouver Mar 2017 Spring School ‘Structural Inference’ 2017, Hamburg Feb 2017 Universite´ de la Coteˆ d’Azur, Nice Jan 2017 ENSAE, Paris Jan 2017 Ecole Polytechnique, Paris Dec 2016 Workshop Learning in High Dimensions with Structure, NIPS, Barcelona Dec 2016 Workshop Adaptive and Scalable Nonparametric Methods in Machine Learning, NIPS, Barcelona Oct 2016 MIT, Boston. Sept 2016 MIT, Boston. July 2016 66th Workshop on Convex Analysis and Optimization, Erice, Italy. June 2016 Brain, Minds, Machines workshop, Sestri Levante, Italy. June 2016 Universitat Pompeu Fabra, Barcellona May 2016 SIAM Conference on Imaging Science, Albuquerque New Mexico May 2016 Plenary Tutorial SIAM Conference on Imaging Science, Albuquerque New Mexico Mar 2016 DALI 2016 Data, Learning and Inference Workshop, Sestri Levante Mar 2016 Hausdorff Research Institute for Mathematics, Bonn Jan 2016 INRIA, Paris Jan 2016 Mathematics of Image Analysis, Paris Jan 2016 Google DeepMind, London Jan 2016 University College London, Dec 2015 New York University, New York Dec 2015 Yahoo!, New York Aug2015 Kyoto University, Japan Jun 2015 University of California, Berkeley May 2015 Applied Inverse Problems Conference, Helsinki May 2015 Deep Learning Workshop, Bertinoro Apr 2015 Neuroengineering Workshop, University of Wisconsin, Madison Apr 2015 DALI 2015 Data, Learning and Inference Workshop, La Palma (Canaries, Spain) Mar 2015 DEI, Universita´ degli Studi di Padova Jan 2015 Optimization and Statistical Learning Workshop , Les Houches, France Dec 2014 Foundations of Computational Mathematics Conference, Universidad de la Republica in Montevideo Nov 2014 Brown University Oct 2014 Simons Institute, University of California, Berkeley Sept 2014 Workshop on ”Optimization and Dynamical Processes for Statistical Learning and Inverse Problems” Sept 2014 Universita’ Bocconi Aug 2014 City University Hong Kong July 2014 IMT Institute for Advanced Studies Lucca June 2014 Mathematical Foundations of Learning Theory Workshop, Barcelona June 2014 International Conference Curves and Surfaces, Paris June 2014 Journee du Labex Bezout Data Science and Massive Data Analysis, ESIEE Paris et l’Ecole des Ponts ParisTech May 2014 University of California at Los Angeles Dec 2013 International Conference Neural Information Processing Systems (NIPS 2013) June 2013 Workshop on Systems, Information, Learning, and Optimization (SILO), University of Wisconsin May 2013 International Conference on Approximation Theory and Applications, Honk Kong Mar 2013 Institute of Science and Technology Austria (IST Austria) Feb 2013 Graduate School of Informatics, Kyoto University Jan 2013 University of Genova Jun 2012 International Conference on System Identiﬁcation Jun 2012 International Conference on Machine Learning, workshop on Reproducing Kernel Hilbert Spaces and Kernel-Based Methods in Machine Learning, Edinburgh. Jun 2012 Oberwolfach, Workshop on Learning Theory and Approximation. Jun 2012 WIAS Berlin. Apr 2012 Ohio State University. Apr 2012 Workshop on Probabilistic techniques and algorithms, University of Texas at Austin. Apr 2012 Computer Science Department, University of Texas at Austin. Mar 2012 University College London, Gatsby Unit, London. Feb 2012 Workshop ”From biology to robots: the iCub project”, MIT. Jan 2012 AFOSR Cognition, Decision & Computational Intelligence Program Review. Dec 2011 Poster Presentation at the International Conference Neural Information Processing Sy- stems (NIPS 2011), Workshop on Challenges in Learning Hierarchical Models: Transfer Learning and Optimization. Aug 2011 Oberwolfach Mini-Workshop: Mathematics of Machine Learning. Jul 2011 Dagstuhl Seminar Computational and Mathematical Foundation of Learning Theory. May 2011 International Conference on Sampling Theory and Applications (SampTA 2011). Dec 2010 Poster Presentation at the International Conference Neural Information Processing Sy- stems (NIPS 2010). Dec 2010 Poster Presentation at the International Conference Neural Information Processing Sy- stems (NIPS 2010). Nov 2010 Ecole´ Polytechqnieue. Nov 2010 Harvard University. Oct 2010 Ohio State University. Sep 2010 European Conference of Machine Learning, Barcelona, Spain. Jul 2010 Radon Institute for Computational and Applied Mathematics. Dec 2012 Poster Presentation at the International Conference on Artiﬁcial Intelligence and Statistics (AISTATS 2010) Mar 2010 Image and Computing Seminar, MIT. Jan 2010 City University of Hong Kong. Dec 2009 Kernels for Multiple Outputs and Multi-task Learning Workshop, International Conferen- ce Neural Information Processing Systems (NIPS 2009). Nov 2009 Pattern Theory Seminar Series, Brown University. Jun 2009 Computational Learning Theory Conference, Montreal. Jun 2009 Machine Learning Summer School/Workshop 2009 University of Chicago. Apr 2009 Duke University. Dec 2008 University of Genova, Genova, Italy. Dec 2008 University College London, London, England. Dec 2008 University of Manchester, Manchester, England. Jul 2008 Societa` Italiana di Matematica Applicata (SIMAI) 9th Congress, September 19, 2008 - Ro- me, Italy. Jul 2008 Societa` Italiana di Matematica Applicata (SIMAI) 9th Congress, September 15, 2008 - Ro- me, Italy. Jul 2008 ICML/COLT-2008 workshop on ”Sparse Optimization and Variable Selection”, Helsinki, Finland. Jul 2008 Oberwolfach workshop, ”Learning Theory and Approximation”, Germany. May 2008 Ohio State University. Dec 2007 Rencontres de statistiques mathematiques,´ Marseille France. Nov 2007 Fuzzy Logic Laboratorium Linz-Hagenberg Johannes Kepler University of Linz. Nov 2007 Radon Institute for Computational and Applied Mathematics. Apr 2007 Duke University, USA. Sep 2006 Conference of the Italian Operations Research Society (AIRO), Cesena, Italy. Jul 2006 21st European Conference on Operational Research, Iceland. Dec 2005 Rencontres de statistiques mathematiques,´ Marseille France. Oct 2005 Workshop on Inverse Problems, Toulouse. Jul 2005 International Conference on Applied Inverse Problems 2005, Royal Agricultural College. Mar 2005 Toyota Technological Institute at Chicago. Dec 2004 Eighteenth Annual Conference on Neural Information Processing Systems. Nov 2004 Department of Computer Science, Queen’s Mary College, London. Nov 2004 Laboratoire PSI FRE CNRS 2645 - INSA de Rouen. Jul 2004 Universite´ de Nice Sophia-Antipolis, Nice, France. Feb 2004 ASTAA Project Workshop, Italy. Oct 2003 University of Modena, Italy. Jun 2003 ASTAA Project Workshop, Italy. Apr 2003 KerMIT Project Workshop, Italy. Nov 2002 ASTAA Project Workshop, Sestri Levante, Italy.