Massimiliano Pontil

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TOMASO POGGIOMcDermott Professor in Brain Sciences, MITPatvirtintas el. paštas ai.mit.edu
Andreas ArgyriouMicrosoft UKPatvirtintas el. paštas alum.mit.edu
Carlo CilibertoUniversity College LondonPatvirtintas el. paštas ucl.ac.uk
Paolo FrasconiProfessor of Computer Science, Università di FirenzePatvirtintas el. paštas dsi.unifi.it
Michele DoniniHelsingPatvirtintas el. paštas helsing.ai
Luca FranceschiAmazon Web ServicesPatvirtintas el. paštas amazon.de
Saverio SalzoAssistant Professor, Sapienza University of Rome, ItalyPatvirtintas el. paštas diag.uniroma1.it
alessandro verriprofessor of computer science, dibris, università degli studi di genovaPatvirtintas el. paštas unige.it
Riccardo GrazziMicrosoft ResearchPatvirtintas el. paštas microsoft.com
Bernardino Romera-ParedesGoogle DeepMindPatvirtintas el. paštas google.com
Karim LouniciStatistics Professor, Ecole PolytechniquePatvirtintas el. paštas polytechnique.edu
Luca OnetoDIBRIS - University of GenoaPatvirtintas el. paštas unige.it
Mark HerbsterUniversity College LondonPatvirtintas el. paštas cs.ucl.ac.uk
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindPatvirtintas el. paštas gatsby.ucl.ac.uk
Luca BaldassarreAI Governance Director, Swiss RePatvirtintas el. paštas swissre.com
Cecilia MascoloUniversity of CambridgePatvirtintas el. paštas cl.cam.ac.uk
Anastasios NoulasNew York University, Center for Data SciencePatvirtintas el. paštas cl.cam.ac.uk
Andre ElisseeffGooglePatvirtintas el. paštas google.com
Vladimir R. KosticFaculty of Science, Novi Sad; IIT, Genova, ItalyPatvirtintas el. paštas dmi.uns.ac.rs
Salvatore ScellatoDeepMind, Google, University of CambridgePatvirtintas el. paštas cl.cam.ac.uk

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Massimiliano Pontil

Istituto Italiano di Tecnologia & University College London

Patvirtintas el. paštas iit.it - Pagrindinis puslapis

Applied Mathematics Learning Theory Machine Learning Statistics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Regularized multi--task learning T Evgeniou, M Pontil Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	2006	2004
Multi-task feature learning A Argyriou, T Evgeniou, M Pontil Advances in neural information processing systems 19, 41, 2007	1888	2007
Convex multi-task feature learning A Argyriou, T Evgeniou, M Pontil Machine learning 73, 243-272, 2008	1797	2008
Regularization networks and support vector machines T Evgeniou, M Pontil, T Poggio Advances in computational mathematics 13, 1-50, 2000	1689	2000
Feature selection for SVMs J Weston, S Mukherjee, O Chapelle, M Pontil, T Poggio, V Vapnik NIPS 12, 668-674, 2000	1590	2000
Support vector machines for 3D object recognition M Pontil, A Verri IEEE transactions on pattern analysis and machine intelligence 20 (6), 637-646, 1998	1258	1998
Learning multiple tasks with kernel methods. T Evgeniou, CA Micchelli, M Pontil Journal of machine learning research 6 (4), 2005	1124	2005
PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments DT Jones, DWA Buchan, D Cozzetto, M Pontil Bioinformatics 28 (2), 184-190, 2012	921	2012
Bilevel programming for hyperparameter optimization and meta-learning L Franceschi, P Frasconi, S Salzo, R Grazzi, M Pontil arXiv preprint arXiv:1806.04910, 2018	903	2018
Optimal kernel choice for large-scale two-sample tests A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ... Advances in neural information processing systems 25, 2012	850	2012
A tale of many cities: universal patterns in human urban mobility A Noulas, S Scellato, R Lambiotte, M Pontil, C Mascolo PloS one 7 (5), e37027, 2012	848	2012
An empirical study of geographic user activity patterns in foursquare A Noulas, S Scellato, C Mascolo, M Pontil Proceedings of the International AAAI Conference on Web and Social Media 5 …, 2011	732	2011
Support vector machines: Theory and applications T Evgeniou, M Pontil Advanced course on artificial intelligence, 249-257, 1999	715	1999
On learning vector-valued functions CA Micchelli, M Pontil Neural computation 17 (1), 177-204, 2005	686	2005
Empirical bernstein bounds and sample variance penalization A Maurer, M Pontil arXiv preprint arXiv:0907.3740, 2009	612	2009
Empirical risk minimization under fairness constraints M Donini, L Oneto, S Ben-David, JS Shawe-Taylor, M Pontil Advances in neural information processing systems 31, 2018	566	2018
Predicting structured data G BakIr, T Hofmann, B Schölkopf, AJ Smola, B Taskar MIT press, 2007	563	2007
Quantum machine learning: a classical perspective C Ciliberto, M Herbster, AD Ialongo, M Pontil, A Rocchetto, S Severini, ... Proc. R. Soc. A 474 (2209), 20170551, 2018	551	2018
Forward and reverse gradient-based hyperparameter optimization L Franceschi, M Donini, P Frasconi, M Pontil International conference on machine learning, 1165-1173, 2017	542	2017
Learning the Kernel Function via Regularization. CA Micchelli, M Pontil Journal of machine learning research 6 (7), 2005	529	2005

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