The yearly Neural Information Processing Systems (NIPS) workshops bring together scientists with broadly varying backgrounds in statistics, mathematics, computer science, physics, electrical engineering, neuroscience, and cognitive science, unified by a common desire to develop novel computational and statistical strategies for information processing and to understand the mechanisms for information processing in the brain. The series editors, in consultation with workshop organizers and members of the NIPS Foundation Board, select specific workshop topics on the basis of scientific excellence, intellectual breadth, and technical impact. Collections of papers chosen and edited by the organizers of specific workshops are built around pedagogical introductory chapters, while research monographs provide comprehensive descriptions of workshop-related topics, to create a series of books that provides a timely, authoritative account of the latest developments in the exciting field of neural computation.
Edited by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer and Neil D. Lawrence
ISBN: 9780262545877
Publisher: The MIT Press
Pub Date: June 7, 2022
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.
Edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston
ISBN: 9780262026253
Publisher: The MIT Press
Pub Date: August 17, 2007
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.
Edited by Guido Dornhege, José del R. Millán, Thilo Hinterberger, Dennis J. McFarland and Klaus-Robert Müller
ISBN: 9780262527880
Publisher: The MIT Press
Pub Date: July 20, 2007
The latest research in the development of technologies that will allow humans to communicate, using brain signals only, with computers, wheelchairs, prostheses, and other devices.
Edited by Gregory Shakhnarovich, Trevor Darrell and Piotr Indyk
ISBN: 9780262195478
Publisher: The MIT Press
Pub Date: March 24, 2006
Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data.
