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- Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio
- AAAI
- 2016

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic… (More)

- Frank Bauer, Sergei V. Pereverzyev, Lorenzo Rosasco
- J. Complexity
- 2007

In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed inverse problems. It is well known that Tikhonov regularization can be profitably used in the… (More)

We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are… (More)

This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream – from V1, V2, V4 to IT – is to discount image… (More)

In this paper, we study a family of gradient descent algorithms to approximate the regression function from Reproducing Kernel Hilbert Spaces (RKHSs), the family being characterized by a polynomial… (More)

- Stephen Smale, Lorenzo Rosasco, Jake V. Bouvrie, Andrea Caponnetto, Tomaso A. Poggio
- Foundations of Computational Mathematics
- 2010

We propose a natural image representation, the neural response, motivated by the neuroscience of the visual cortex. The inner product defined by the neural response leads to a similarity measure… (More)

- Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Umberto De Giovannini, Francesca Odone
- Journal of Machine Learning Research
- 2005

Many works related learning from examples to regularization techniques for inverse problems, emphasizing the strong algorithmic and conceptual analogy of certain learning algorithms with… (More)

- Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, Alessandro Verri
- Neural Computation
- 2004

In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory. We introduce a convexity assumption, which is met by all loss… (More)

- Christine De Mol, Ernesto De Vito, Lorenzo Rosasco
- J. Complexity
- 2009

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie [H. Zou, T. Hastie, Regularization and variable… (More)