Andreas Argyriou

• Tensors, Kernels, and Machine Learning (NIPS 2010 workshop).

• A brief introduction to multi-task learning and kernel learning (NIPS workshop on kernel learning 2008).

(longer version)

Publications

• A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs (with E. Richard, T. Evgeniou and N. Vayatis). Technical report.

• Exploiting Unrelated Tasks in Multi-Task Learning (with B. Romera-Paredes, N. Berthouze and M. Pontil). AISTATS 2012.

• A General Framework for Structured Sparsity via Proximal Optimization (with L. Baldassarre, J. Morales and M. Pontil). AISTATS 2012.

• Efficient First Order Methods for Linear Composite Regularizers (with C. A. Micchelli, M. Pontil, L. Shen and Y. Xu). Technical report (and accompanying Matlab code).

• A Study of Convex Regularizers for Sparse Recovery and Feature Selection Technical report.

• On Spectral Learning (with C. A. Micchelli and M. Pontil). Journal of Machine Learning Research, 11:935-953, 2010.

• When Is There a Representer Theorem? Vector versus Matrix Regularizers (with C. A. Micchelli and M. Pontil). Journal of Machine Learning Research, 10:2507-2529, 2009.

• An Algorithm for Transfer Learning in a Heterogeneous Environment (with A. Maurer and M. Pontil). ECML 2008.

• Convex Multi-Task Feature Learning (with T. Evgeniou and M. Pontil). Machine Learning, 73, 3, 243-272, Special Issue on Inductive Transfer Learning, 2008.

• A Spectral Regularization Framework for Multi-Task Structure Learning (with C. A. Micchelli, M. Pontil and Y. Ying). NIPS 2007.

• Learning to Integrate Data from Different Sources and Tasks PhD Thesis.

• Multi-Task Feature Learning (with T. Evgeniou and M. Pontil). NIPS 2006.

• A DC-Programming Algorithm for Kernel Selection (with R. Hauser, C. A. Micchelli and M. Pontil). ICML 2006.

• Combining Graph Laplacians for Semi--Supervised Learning (with M. Herbster and M. Pontil). NIPS 2005.

• Learning Convex Combinations of Continuously Parameterized Basic Kernels (with C.A. Micchelli and M. Pontil). COLT 2005.

• Efficient Approximation Methods for Harmonic Semi-Supervised Learning Master's Thesis.

Software

Curriculum Vitae

Advanced Topics in Machine Learning course (UCL, spring 2009)