Selected publications

Here are some of my research papers. If you have questions or comments please feel free to contact me.
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2011

• Structured Sparsity and Generalization (with A. Maurer) arXiv:1108.3476, 2011.
• Exploiting semantic annotations for clustering geographic areas and users in location-based social networks (with A. Noulas, S. Scellato and C. Mascolo) SMW 2011.
• An empirical study of geographic user activity patterns in foursquare (with A. Noulas, S. Scellato and C. Mascolo) ICWSM 2011.
• Efficient first order methods for linear composite regularizers (with A. Argyriou, C.A. Micchelli, L. Shen and Y. Xu) arXiv:1104.1436, 2011.
• Regularizers for structured sparsity (with C.A. Micchelli and J.M. Morales) Advances in Computational Mathematics (to appear).
• Oracle inequalities and optimal inference under group sparsity (with K. Lounici, A.B. Tsybakov and S. van de Geer), Annals of Statistics 39(4): 2164-2204, 2011.
• Predictions of hot spot residues at protein-protein interfaces using support vector machines (with S. Lise, D. Buchan and D.T. Jones), PLoS One 6(2):e16774, 2011.

2010

• A family of penalty functions for structured sparsity (with C.A. Micchelli and J.M. Morales) NIPS 2010.
• K-dimensional coding schemes in Hilbert spaces (with A. Maurer) IEEE Transactions on Information Theory, 56(11): 5839-5846, 2010.
• On spectral learning (with A. Argyriou and C.A. Micchelli) J. Machine Learning Research, 11:935-953, 2010.

2009

• Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods (with S. Lise, C. Archambeau and D.T. Jones), BMC Bioinformatics, 10:365-382, 2009.
• Inferring interests from mobility and social interactions (with A. Noulas, M. Musolesi and C. Mascolo), NIPS Workshop on Analyzing Networks and Learning With Graphs, 2009.
• When is there a representer theorem? Vector versus matrix regularizers (with A. Argyriou and C.A. Micchelli), J. Machine Learning Research, 10:2507-2529, 2009.
• Taking advantage of sparsity in multi-task learning (with K. Lounici, A.B. Tsybakov and S. van de Geer) COLT 2009.
• Empirical Bernstein bounds and sample-variance penalization (with A. Maurer) COLT 2009.
• Entropy conditions for Lr-convergence of empirical processes (with A. Caponnetto and E. De Vito) Advances in Computational Mathematics, 30(4):355-373, 2009.

2008

• Online prediction on large diameter graphs (with M. Herbster and G. Lever) NIPS 2008.
• Fast prediction on a tree (with M. Herbster and S. Rojas-Galeano) NIPS 2008.
• A uniform lower error bound for half-space learning (with A. Maurer) ALT 2008.
• Generalization bounds for K-dimensional coding schemes in Hilbert spaces (with A. Maurer) ALT 2008.
• An algorithm for transfer learning in a heterogeneous environment (with A. Argyriou and A. Maurer) ECML 2008.
• Universal multi-task kernels (with A. Caponnetto, C.A. Micchelli and Y. Ying) J. Machine Learning Research, 9:1615-1646, 2008.
• Convex multi-task feature learning (with A. Argyriou and T. Evgeniou) Machine Learning 73(3):243-272, 2008.
• Online gradient descent learning algorithms (with Y. Ying) Foud. Comp. Math. 8(5):561-596, 2008.

2007

• A spectral regularization framework for multi-task structure learning (with A. Argyriou, C.A. Micchelli and Y. Ying) NIPS 2007.
• A convex optimization approach to modeling heterogeneity in conjoint estimation (with T. Evgeniou and O. Toubia) Marketing Science, 26:805-818, 2007.
• Feature space perspectives for learning the kernel (with C.A. Micchelli) Machine Learning, 66:297-319, 2007.
• Conditional graphical models (with F. Perez-Cruz and Z. Ghahramani) In Predicting Structured Data, edited by G. Bakir et al., MIT Press, 2007.

2006

• Multi-task feature learning (with A. Argyriou and T. Evgeniou) NIPS 2006.
• Prediction on a graph with the perceptron (with M. Herbster) NIPS 2006.
• A DC algorithm for kernel selection (with A. Argyriou, R. Hauser and C.A. Micchelli) ICML 2006.
• Error analysis for online gradient descent algorithms in reproducing kernel Hilbert spaces (with Y. Ying and D-X. Zhou) Preprint, April 2006.

2005

• Combining graph Laplacians for semi-supervised learning (with A. Argyriou and M. Herbster) NIPS 2005.
• Error bounds for learning the kernel (with C.A. Micchelli, Q. Wu, and D.X. Zhou) Research Note RN/05/09, Dept of Computer Science, UCL, June, 2005.
• Online learning over graphs (with M. Herbster and L. Wainer) ICML 2005.
• Learning convex combinations of continuously parameterized basic kernels (with A. Argyriou and C.A. Micchelli) COLT 2005.
• Learning the kernel function via regularization (with C.A. Micchelli) J. Machine Learning Research, 6:1099-1125, 2005.
• Learning multiple tasks with kernel methods (with T. Evgeniou and C.A. Micchelli) J. Machine Learning Research, 6:615-637, 2005.
• Wide coverage natural language processing using kernel methods and neural networks for structured data (with F. Costa, P. Frasconi and S. Menchetti) Pattern Recognition Letters, 26(12):1896-1906, 2005.
• On learning vector-valued functions (with C.A. Micchelli) Neural Computation, 17:177-204, 2005.
• Stability of randomized learning algorithms (with A. Elisseeff and T. Evgeniou) J. Machine Learning Research, 6: 55-79, 2005 (See also the longer version: Stability of randomized learning algorithms with an application to bootstrap methods).

2004

• Kernels for multi-task learning (with C.A. Micchelli) NIPS 2004.
• Regularized multi-task learning (with T. Evgeniou) SIGKDD 2004
• A function representation for learning in Banach spaces (with C.A. Micchelli) COLT 2004.
• New results on error correcting output codes of kernel machines (with A. Passerini and P. Frasconi) IEEE Trans. on Neural Networks, 15(1):45-54, 2004.
• Leave-one-out error, stability, and generalization of voting combination of classifiers (with A. Elisseeff and T. Evgeniou) Machine Learning, 55(1):71-97, 2004.

2003

• A note on different covering numbers in learning theory. Journal of Complexity, 19:665-671, 2003.
• Leave-one-out error and stability of learning algorithms with applications (with A. Elisseeff) In Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, J. Suykens et al. Eds., IOS press, 2003.
• Learning in reproducing kernel Hilbert spaces: a guide tour. Bull. of the Italian Artificial Intelligence Association - AI*IA Notizie, 2003.
• On different ensembles of kernel machines (with M. Yamana, H. Nakahara and S. Amari) ESANN 2003.
• Image representations and feature selection for multimedia database search (with T. Evgeniou, T. Poggio, and C. Papageorgiou) IEEE Trans. on Knowledge and Data Engineering, 15(4):911-920, 2003.
• Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines (with Y. Yao, G. Marcialis, P. Frasconi, and F. Roli) Pattern Recognition, 36(2):397-406, 2003.
• Full body person recognition (with C. Nakajima, B. Heisele, and T. Poggio) Pattern Recognition, 36:1997-2006, 2003.

2002

• A simple algorithm for learning stable machines (with S. Andonova, A. Elisseeff and T. Evgeniou) ECAI 2002.
• Regularization and statistical learning theory for data analysis (with T. Evgeniou, T. Poggio and A. Verri) Computational Statistics and Data Analysis, 38:421-432, 2002.
• Support vector machines with clustering for training with very large datasets (with T. Evgeniou) SETN 2002.

2001

• Categorization by learning and combining object parts (with B. Heisele, T. Serre, T. Vetter and T. Poggio) NIPS 2001.
• Component-based face detection (with B. Heisele, T. Serre and T. Poggio) CVPR 2001.
• Fingerprint classification with combination of support vector machines (with Y. Yao and P. Frasconi) AVBPA 2001.

2000

• On the noise model of support vector machine regression (with S. Mukherjee and F. Girosi) ALT 2000.
• Face detection in still gray images (with B. Heisele and T. Poggio) AI Memo 1687, Massachusetts Institute of Technology, May 2000.
• Statistical learning theory: a primer (with T. Evgeniou and T. Poggio) Int. Journal of Computer Vision, 38(1):9-13, 2000.
• Bounds on the generalization performance of kernel machines ensembles (with T. Evgeniou, L. Perez-Breva and T. Poggio) ICML 2000.
• Regularization networks and support vector machines (with T. Evgeniou and T. Poggio) Advances in Computational Mathematics, 13(1):1-50, 2000.
• Feature selection for SVMs (with J. Weston, S. Mukherjee, O. Chapelle, T. Poggio, and V. Vapnik) NIPS 2000.

1999

• From regression to classification in support vector machines (with R. Rifkin and T. Evgeniou) ESANN 1999.
• On the V-gamma dimension for regression in reproducing kernel Hilbert spaces. (with T. Evgeniou) ALT 1999.

1998

• Properties of support vector machines (with A. Verri) Neural Computation, 10:955-974, 1998.
• Support vector machines for 3D object recognition (with A. Verri) IEEE Trans. on PAMI, 20(6):637-646, 1998.