PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Massimiliano Pontil

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Number of EPrints submitted by this user: 19

Learning the kernel function via regularization
Massimiliano Pontil and Charles Micchelli
(2004) Technical Report. UCL, UK.

Regularized Multi--Task Learning
Massimiliano Pontil and Theos Evgeniou
In: KDD 2004(2004).

Learning Convex Combinations of Continuously Parameterized Basic Kernels
Andreas Argyriou, Charles Micchelli and Massimiliano Pontil
In: COLT 2005, June 2005, Bertinoro, Italy.

Online Learning over Graphs
Mark Herbster, Massimiliano Pontil and Lisa Wainer
In: ICML 2005, Bonn, Germany(2005).

Error bounds for learning the kernel
Charles Micchelli, Massimiliano Pontil, Qiang WU and Ding-Xuan Zhou
Research Note Number 05/09, pp. 1-14, 2005.

Multi-Task Feature Learning
Andreas Argyriou, Theodoros Evgeniou and Massimiliano Pontil
In: NIPS 2006, Vancouver, CA(2007).

Prediction on a Graph with the Perceptron
Mark Herbster and Massimiliano Pontil
In: NIPS 2006, December 2006, Vancouver, CA.

A DC algorithm for kernel selection
Andreas Argyriou, Raphael Hauser, Charles Micchelli and Massimiliano Pontil
In: ICML 2006(2006).

Combining graph Laplacians for semi--supervised learning
Andreas Argyriou, Mark Herbster and Massimiliano Pontil
In: NIPS 2005, December, 2005, Vancouver, CA.

A Spectral Regularization Framework for Multi-Task Structure Learning
Andreas Argyriou, Charles Micchelli, Massimiliano Pontil and Yiming Ying
In: NIPS 2007, Dec 2007, Vancouver, Canada.

Universal multi-task kernels
Andreas Caponnetto, Charles Micchelli, Massimiliano Pontil and Yiming Ying
(2007) Technical Report. UCL.

Entropy conditions for Lr-convergence of empirical processes
Andrea Caponnetto, Ernesto De Vito and Massimiliano Pontil
(2007) Other. UCL.

Convex Multi-Task Feature Learning
Andreas Argyriou, Theodoros Evgeniou and Massimiliano Pontil
Machine Learning 2006.

Feature space perspectives for learning the kernel
Charles Micchelli and Massimiliano Pontil
Machine Learning 2007.

A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation
Theodoros Evgeniou, Massimiliano Pontil and Olivier Toubia
Marketing Science 2007.

When is there a representer theorem? Vector versus matrix regularizers
Andreas Argyriou, Charles Micchelli and Massimiliano Pontil
Submitted to J. Mach. Learn. Research 2008.

A Uniform Lower Error Bound for Half-space Learning
Andreas Maurer and Massimiliano Pontil
In: Algorithmic Learning Theory, 13-16 Oct 2008, Budapest.

Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
Andreas Maurer and Massimiliano Pontil
In: Algorithmic Learning Theory 2008, 13-16 Oct 2008, Budapest.

An Algorithm for Transfer Learning in a Heterogeneous Environment
Andreas Argyriou, Andreas Maurer and Massimiliano Pontil
In: ECML 2008(2008).