preprints

journal papers

  1. Tacchetti, A. , Mallapragada, P., Santoro, M. and Rosasco, L.
    GURLS: a Least Squares Library for Supervised Learning.
    To appear in Journal of Machine Learning Research, also ArXiv:1303.0934.
  2. De Vito, E. Rosasco, L. and Toigo, A.
    Learning Sets with Separating Kernels
    to appear in the Journal of Applied and Computational Harmonic Analysis (ArXiv:1204.3573).
  3. Villa, S., Mosci,  and Rosasco, L.
    Proximal methods for the latent group lasso penalty
    to appear in the Journal of Computational Optimization and Applications (Arxiv1209.0368).
  4. Rosasco, L., Villa, S., Mosci, S., Santoro, M., and Verri, A.
    Nonparametric Sparsity and Regularization
    Journal of Machine Learning Research, 14(Jul):1665−1714, 2013.
  5. Alvarez, M., Rosasco, L. and Lawrence, N.
    Kernels for Vector-Valued Functions: a Review
    To appear in Foundations and Trends in Machine Learning, arXiv:1106.6251.
  6. Baldassarre, L., Barla, A., Rosasco, L. and Verri, A.
    Multi-Output Learning via Spectral Filtering
    Machine Learning 83(3).
  7. Mosci, S., Rosasco, L., Verri, A. and Villa, S,
    Applications of Variational Convergence to Regularized Learning Algorithms
    To be published in Optimization.
  8. De Vito, E., Pereverzev, S. and Rosasco, L.
    Adaptive Learning via the Balancing Principle
    Foundations of Computational Mathematics, 8 355-479 (2010).
  9. 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 identies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients
    Molecular Cancer 2010, 9:185.
  10. 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)
  11. Smale S., Rosasco L., Bouvrie, J., Caponnetto, A. and Poggio, T.
    Mathematics of the Neural Response
    Foundations of Computational Mathematics, June 2009, DOI 10.1007/s10208-009-9049-1.
  12. Paolo Fardin, Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Alessandro Verri, Luigi Varesio.
    The l1-l2 regularization framework unmasks the hypoxic signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines
    to appear in BMC Genomics.
  13. Valerio Del Bono, Alessandra Mularoni, Elisa Furfaro, Emanuele Delfino, Lorenzo Rosasco, Franca Miletich, and Claudio Viscoli
    Clinical Evaluation of a (1,3)-β-D-Glucan Assay for Presumptive Diagnosis of Pneumocystis jiroveci Pneumonia in Immunocompromised Patients
    Clin. Vaccine Immunol. 2009 16: 1524-1526. 
  14. Rosasco, L., Belkin, M., and De Vito, E.
    On Learning with Integral Operators
    Journal of Machine Learning Research 11(Feb):905?934, 2010.
  15. De Mol, C., De Vito E. and Rosasco L.
    Elastic Net Regularization in Learning Theory
    to appear in the Journal of Complexity (also CBCL paper #273/ CSAILTechnical Report #TR-2008-046,
    Massachusetts Institute of Technology, Cambridge, MA, July 24, 2008 and arXiv:0807.3423).
  16. Lo Gerfo L., Rosasco L., Odone F., De Vito E. and Verri, A.
    Spectral Algorithms for Supervised Learning
    Neural Comput. 2008 20: 1873-1897.
  17. Bauer F., Pereverzev S. and Rosasco L.
    On Regularization Algorithms in Learning Theory
    J. Complexity 23(1): 52-72 (2007) (Technical Report DISI-TR-05-19).
  18. Yao Y., Rosasco L. and Caponnetto, A.
    On Early Stopping in Gradient Descent Learning
    Constr. Approx. 26 (2007), no. 2, 289–315.
  19. De Vito E., Rosasco L. and Caponnetto, A.
    Discretization Error Analysis for Tikhonov Regularization
    Analysis and Applications Vol. 4, No. 1 (January 2006).
  20. De Vito E., Rosasco L.,Caponnetto A., De giovannini U. and Odone F.
    Learning from Examples as an Inverse Problem
    Journal of Machine Learning Research 6(May):883--904, 2005.
  21. 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. .
  22. De Vito E., Caponnetto A, 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 .
  23. Rosasco L., Caponnetto A., De Vito E., Piana M. and Verri A.
    Are Loss Function All the Same?

    Neural Computation, Vol. 16, Issue 5 - May 2004.

chapters in books

  1. G. Chen, A.V. Little, M. Maggioni, L. Rosasco
    Some recent advances in multiscale geometric analysis of point clouds,
    Wavelets and Multiscale Analysis: Theory and Applications (March, 2010), Springer.

conference papers and extended abstracts

  1. 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.
  2. Rudi, A., Canas, G., and Rosasco, L.
    On the Sample Complexity of Subspace Learning
    In Advances in Neural Information Processing Systems (NIPS) 26. 2013.
  3. Ciliberto,C.,Fanello,S..Santoro,M.,Natale,L.,Metta,G.andRosasco,L.
    On the Impactof Learning Hierarchical Representations for Visual Recognition in Robotics
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013.
  4. 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.
  5. Mroueh, Y., Rosasco, L.
    Q-ary Compressive Sensing.
    Proceedings SampTA, 2013, also Arxiv 1302.5168.
  6. Mroueh,Y.,Poggio,T.,Rosasco,L.Slotine,J.J.
    Multi-class Learning with Simplex Coding.
    InAdvancesinNeural Information Processing Systems, NIPS 2012.
  7. Canas, G.D., Rosasco, L., Poggio, T.
    Learning Manifolds with K-Means and K-Flats.
    In Advances in Neural Information Processing Systems, NIPS 2012.
  8. Canas, G.D., Rosasco, L.
    Learning Probability Measures with respect to Optimal Transport Metrics.
    In Advances in Neural Information Processing Systems, NIPS 2012.
  9. A.Tacchetti, P.Mallapragada, M.Santoro and L. Rosasco,
    GURLS: a Least Squares Toolbox for Supervised Learning
    NIPS 2011, Workshop on Big Learning: Algorithms, Systems, and Tools for Learning at Scale, Sierra Nevada
    Spain, December 2011.
  10. A.V. Little, M. Maggioni, L. Rosasco
    Multiscale Geometric Methods for Estimating Intrinsic Dimension
    Proc. SampTA 2011 (2010).
  11. De Vito, E., Rosasco, L. and Toigo, A.
    Support Estimation via Spectral Regularization
    Proc. SampTA 2011 (2010).
  12. De Vito, E., Rosasco, L. and Toigo, A.
    Spectral Regularization for Support Estimation
    Advances in Neural Information Processing Systems (NIPS) 23, 2010.
  13. 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.
  14. Mosci, S., Villa, S. and Rosasco, L.
    A fast algorithm for structured gene selection
    Fourth International Workshop on Machine Learning in Systems Biology.
  15. Mosci, S., Villa, S. and Rosasco, L.
    Combining l1-l2 regularization with biological prior for multi-level hypoxia signature in Neuroblastoma.
    Fourth International Workshop on Machine Learning in Systems Biology.
  16. Baldassarre, L., Barla, A., Rosasco, L. and Verri, A.
    Learning Vector Fields via Spectral Filtering
    Proc. of ECML 2010.
  17. Mosci, S., Rosasco, L., Santoro, M., Verri, A. and Villa, S.
    Solving Structured Sparsity Regularization with Proximal Methods.
    ECML 2010.
  18. Rosasco, L., Mosci, S., Santoro, M., Verri, A. and Villa, S.
    A Regularization Approach to non linear Variable Selection
    Proc. of AISTAT 2010.
  19. Bouvrie, J., Rosasco, L. and Poggio, T.
    On Invariance in Hierarchical Models
    Advances in Neural Information Processing Systems (NIPS) 22, 2009.
  20. Frogner, C., Bristow, C., Morgan, T., Rosasco, L., Poggio, T. and Kellis, M.
    Learning recurrent mRNA expression patterns from systematic analysis of in-situ images of Drosophila embryos
    RECOMB System Biology (poster).
  21. Rosasco, L., Belkin, M., and De Vito, E.
    A Note on Learning with Integral Operators,
    COLT 2009-- 22nd Annual Conference on Learning Theory.
  22. 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. 
  23. S. Mosci, A. Barla, A. Verri and L. Rosasco.
    Finding Structured Gene Signatures,
    BIBM, 2008-- IEEE International Conference on Bioinformatics and Biomedicine.
  24. Rosasco L. (joint work with De Mol, C., De Vito E.)
    Analysis of Elastic Net Regularization,
    OBERWOLFACH Report, Learning Theory and Approximation Workshop, Mathematisches Forschungsinstitut Oberwolfach.
  25. Rosasco L. (joint work with De Mol, C., De Vito E.)
    Elastic net regularization in learning theory,
    ICML/UAI/COLT 2008 Joint Workshops-- Sparse Optimization and Variable Selection Workshop, .
  26. Barla, A., Mosci, S., Rosasco, L. and Verri, A.
    A method for robust variable selection with significance assessments"
    ESANN-- 16th European Symposium on Artificial Neural Networks.
  27. Mosci, S., Rosasco, L. and Verri A.
    Dimensionality reduction and generalization,
    ICML 2007-- Proceedings of the 24th International Conference on Machine Learning.
  28. Caponnetto A., Rosasco L., Odone F. and Verri A.
    Support Vectors Algorithms as Regularization Networks,
    Esann 2005-- 13th European Symposium on Artificial Neural Networks.
  29. Rosasco L., Caponnetto A., De Vito E., De giovannini U. and Odone F.
    Learning, Regularization and Ill-posed Inverse problems,
    NIPS 2004-- Eighteenth Annual Conference on Neural Information Processing Systems.

technical reports

  1. 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.
  2. 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, Cambridge, MA, December 31,
    2010.
  3. Wibisono, A., J. Bouvrie, L. Rosasco, and T. Poggio
    Learning and Invariance in a Family of Hierarchical Kernels.
    MIT-CSAIL-TR-2010-035 / CBCL-290, Massachusetts Institute of Technology, Cambridge, MA, July 30, 2010
  4. Bouvrie, J., Rosasco, L. , Shakhnarovich, G. and Smale, S.
    Notes on the Shannon Entropy of the Neural Response.
    CBCL-281, MIT-CSAIL-TR-2009-049, Massachusetts Institute of Technology, Cambridge, MA, October 9, 2009.
  5. Rosasco, L., Belkin, M., and De Vito, E.
    "A Note on Perturbation Results for Learning Empirical Operators"
    CBCL paper #274/ CSAIL Technical Report #TR-2008-052 , Massachusetts Institute of Technology, Cambridge, MA, August 19, 2008.
  6. De Vito, E., Pereverzev S. and Rosasco L.
    "Adaptive Kernel Methods via the Balancing Principle",
    CBCL paper #275/CSAIL Technical Report#TR-2008-062 Massachusetts Institute of Technology, Cambridge, MA, October 16, 2008
  7. De Mol, C., De Vito E. and Rosasco L.
    "Elastic Net Regularization in Learning Theory",
    CBCL paper #273/ CSAILTechnical Report #TR-2008-046,
    Massachusetts Institute of Technology, Cambridge, MA, July 24, 2008.
    arXiv:0807.3423 (submitted).
  8. Rosasco L., De Vito E. and Verri A.
    " Spectral Methods for Regularization in Learning Theory",
    Technical report DISI-TR-05-18.
  9. Caponnetto A., Rosasco L., De Vito E. and Verri A.
    "Empirical Effective Dimension and Optimal Rates for Regularized Least Squares Algorithm ",
    CBCL Paper #252/AI Memo #2005-019, MIT, Cambridge, MA, May 2005.
  10. Caponnetto A. and Rosasco L.
    " Non Standard Support Vector Machines and Regularization Networks",
    DISI-TR-04-03.
  11. De Vito E., Rosasco L., Caponnetto A., Piana M. and Verri A.
    "Representer Theorem for Convex Loss Fuction",
    DISI-TR-03-13.
  12. De Vito E., Rosasco L., Caponnetto A., Piana M. and Verri A.
    " Minimization of Tiklhonov Functional: the Continuos Setting",
    DISI-TR-03-14 .