Below are some of my research papers. If you have questions or comments please feel free to contact me.
An up-to-date list of my publications can be found at Google Scholar here.
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<!The complete list of my publications is available here.->
A new convex relaxation for tensor completion (with B. Romera-Paredes) NIPS 2013.
Regularized robust portfolio estimation (with T. Evgeniou, D. Spinellis, R. Swiderski and N. Nassuphis), 2013.
Multilinear multitask learning (with B. Romera-Paredes, H. Aung and N. Bianchi-Berthouze) ICML 2013.
Sparse coding for multitask and transfer learning (with A. Maurer and B. Romera-Paredes) ICML 2013.
On sparsity inducing regularization methods for machine learning (with A. Argyriou, L. Baldassarre and C. A. Micchelli)
In Empirical Inference, Festschrift in Honor of Vladimir N. Vapnik,
(B. Scholkopf et al. eds.)
Excess risk bounds for multitask learning with trace norm regularization
(with A. Maurer) COLT 2013.
Transfer learning to account for idiosyncrasy in face and body expressions (with B. Romera-Paredes, H. Aung, A. C. Williams, P. Watson, N. Bianchi-Berthouze) FG 2013.
Regularizers for structured sparsity (with C. A. Micchelli and J. M. Morales) Advances in Computational Mathematics 38(3):455-489, 2013.
Optimal kernel choice for large-scale two-sample tests (with A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, K. Fukumizu) NIPS 2012.
Conditional mean embeddings as regressors
(with S. Grünewälder, G. Lever, L. Baldassarre, S. Patterson, A. Gretton) ICML 2012.
Modelling transition dynamics in MDPs with RKHS embeddings
(with S. Grünewälder, G. Lever, L. Baldassarre, A. Gretton) ICML 2012.
Structured sparsity models for brain decoding from fMRI data
(with L. Baldassarre and J. Mourao-Miranda) PRNI 2012.
A tale of many cities: universal patterns in human urban mobility
(with A. Noulas, S. Scellato, R. Lambiotte and C. Mascolo)
PLoS One 7(5):e37027, 2012.
Exploiting unrelated tasks in multi-task learning (with B. Romera-Paredes,
A. Argyriou and N. Bianchi-Berthouze) AISTATS 2012.
A general framework for structured sparsity via proximal optimization
(with L. Baldassarre, J. M. Morales and A. Argyriou) AISTATS 2012.
Structured sparsity and generalization
(with A. Maurer) J. Machine Learning Research, 13:671-690, 2012.
PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignements
(with D. T. Jones, D. W. A. Buchan and D. Cozzetto) Bioinformatics, 28(2):184-190, 2012.
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,
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,
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.
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)
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.
(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).
A note on
different covering numbers in learning theory. Journal of Complexity,
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.
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)
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