@conference {1572, title = {Less is More: Nystr{\"o}m Computational Regularization}, booktitle = {NIPS 2015}, year = {2015}, abstract = {

We study Nystr"om 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 considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nystr"om Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

}, url = {https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization}, author = {Alessandro Rudi and Raffaello Camoriano and Lorenzo Rosasco} } @article {461, title = {Abstracts of the 2014 Brains, Minds, and Machines Summer Course}, number = {024}, year = {2014}, month = {09/2014}, abstract = {

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.

}, author = {Nadav Amir and Tarek R. Besold and Raffaello Camoriano and Goker Erdogan and Thomas Flynn and Grant Gillary and Jesse Gomez and Ariel Herbert-Voss and Gladia Hotan and Jonathan Kadmon and Scott W. Linderman and Tina T. Liu and Andrew Marantan and Joseph Olson and Garrick Orchard and Dipan K. Pal and Giulia Pasquale and Honi Sanders and Carina Silberer and Kevin A Smith and Carlos Stein N. de Briton and Jordan W. Suchow and M. H. Tessler and Guillaume Viejo and Drew Walker and Leila Wehbe and Andrei Barbu and Leyla Isik and Emily Mackevicius and Yasmine Meroz} }