- Online Learning over Graphs Mark Herbster m.herbster@cs.ucl.ac.uk
- Journal of Machine Learning Research 6 (2005) 615637 Submitted 2/05; Published 4/05 Learning Multiple Tasks with Kernel Methods
- Open House on Multi-Task and Complex Outputs Learning (July 1014, 2006)
- Convex Multi-Task Feature Learning Andreas Argyriou1, Theodoros Evgeniou2, and Massimiliano Pontil1
- Efficient First Order Methods for Linear Composite Regularizers
- A Family of Penalty Functions for Structured Charles A. Micchelli
- Inferring Interests from Mobility and Social Interactions
- Journal of Machine Learning Research 10 (2009) 2507-2529 Submitted 9/08; Revised 3/09; Published 11/09 When Is There a Representer Theorem?
- Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
- An Algorithm for Transfer Learning in a Heterogeneous Environment
- Online gradient descent learning algorithms Yiming Ying and Massimiliano Pontil
- A Spectral Regularization Framework for Multi-Task Structure Learning
- A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation
- Multi-Task Feature Learning Andreas Argyriou
- A DC-Programming Algorithm for Kernel Selection Andreas Argyriou a.argyriou@cs.ucl.ac.uk
- Combining Graph Laplacians for SemiSupervised Learning
- Journal of Machine Learning Research 6 (2005) 5579 Submitted 2/04; Revised 8/04; Published 1/05 Stability of Randomized Learning Algorithms
- Stability of Randomized Learning Algorithms with an Application to Bootstrap Andre Elisseeff
- Kernels for Multitask Learning Charles A. Micchelli
- Regularized MultiTask Learning Theodoros Evgeniou
- Leave one out error, stability, and generalization of voting combinations of classifiers
- A Note on Different Covering Numbers in Learning Theory Massimiliano Pontila
- ESANN'2003 proceedings -European Symposium on Artificial Neural Networks Bruges (Belgium), 23-25 April 2003, d-side publi., ISBN 2-930307-03-X, pp. 197-201
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- PROJECT DESCRIPTIONS DRAFT -13/3/03
- Feature space perspectives for learning the kernel1 Charles A. Micchelli
- Journal of Machine Learning Research 6 (2005) 10991125 Submitted 10/04; Revised 2/05; Published 7/05 Learning the Kernel Function via Regularization
- Computational Statistics & Data Analysis 38 (2002) 421432 www.elsevier.com/locate/csda
- Support Vector Machines with Clustering for Training with Very Large Datasets
- Prediction on a Graph with a Perceptron Mark Herbster, Massimiliano Pontil
- ON SPECTRAL LEARNING On Spectral Learning
- Error analysis for online gradient descent algorithms in reproducing kernel Hilbert spaces
- MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
- Fast Prediction on a Tree Mark Herbster, Massimiliano Pontil, Sergio Rojas-Galeano
- CURRICULUM VITAE of MASSIMILIANO PONTIL Department of Computer Sciences, University College London
- A Uniform Lower Error Bound for Half-space Andreas Maurer1
- Bounds on the Generalization Performance of Kernel Machine Ensembles
- A Simple Algorithm for Learning Stable Machines Savina Andonova
- Research Note RN/03/08 Department of Computer Science, University College London
- Taking Advantage of Sparsity in Multi-Task Learning Karim Lounici
- Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks
- An Empirical Study of Geographic User Activity Patterns in Foursquare Anastasios Noulas
