Andreas Argyriou

According to our database, Andreas Argyriou authored at least 23 papers between 2005 and 2020.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2020

Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems.

Andreas Argyriou, Miguel González-Fierro, Le Zhang

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

In Search for a Cure: Recommendation With Knowledge Graph on CORD-19.

Iris Shen, Le Zhang, Jianxun Lian, Chieh-Han Wu, Miguel González-Fierro, Andreas Argyriou, Tao Wu

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2015

Convex relaxations of penalties for sparse correlated variables with bounded total variation.

Eugene Belilovsky, Andreas Argyriou, Gaël Varoquaux, Matthew B. Blaschko

Mach. Learn., 2015

2014

Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization.

Andreas Argyriou, Marco Signoretto, Johan A. K. Suykens

CoRR, 2014

A Unifying View of Representer Theorems.

Andreas Argyriou, Francesco Dinuzzo

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

On Sparsity Inducing Regularization Methods for Machine Learning.

Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil

Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012

Exploiting Unrelated Tasks in Multi-Task Learning.

Bernardino Romera-Paredes, Andreas Argyriou, Nadia Berthouze, Massimiliano Pontil

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A General Framework for Structured Sparsity via Proximal Optimization.

Luca Baldassarre, Jean Morales, Andreas Argyriou, Massimiliano Pontil

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

PRISMA: PRoximal Iterative SMoothing Algorithm

Francesco Orabona, Andreas Argyriou, Nathan Srebro

CoRR, 2012

Sparse Prediction with the k-Overlap Norm

Andreas Argyriou, Rina Foygel, Nathan Srebro

CoRR, 2012

A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs

Emile Richard, Andreas Argyriou, Theodoros Evgeniou, Nicolas Vayatis

CoRR, 2012

Sparse Prediction with the $k$-Support Norm.

Andreas Argyriou, Rina Foygel, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Efficient First Order Methods for Linear Composite Regularizers

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Lixin Shen, Yuesheng Xu

CoRR, 2011

2010

On Spectral Learning.

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

J. Mach. Learn. Res., 2010

2009

When Is There a Representer Theorem? Vector Versus Matrix Regularizers.

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

J. Mach. Learn. Res., 2009

2008

Learning to integrate data from different sources and tasks.

Andreas Argyriou

PhD thesis, 2008

Convex multi-task feature learning.

Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

Mach. Learn., 2008

An Algorithm for Transfer Learning in a Heterogeneous Environment.

Andreas Argyriou, Andreas Maurer, Massimiliano Pontil

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

2007

A Spectral Regularization Framework for Multi-Task Structure Learning.

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Multi-Task Feature Learning.

Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A DC-programming algorithm for kernel selection.

Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil

Proceedings of the Machine Learning, 2006

2005

Combining Graph Laplacians for Semi-Supervised Learning.

Andreas Argyriou, Mark Herbster, Massimiliano Pontil

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Learning Convex Combinations of Continuously Parameterized Basic Kernels.

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005