The annual Neural Information Processing Systems 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. As opposed to conferences, these workshops maintain a flexible format that encourages the presentation and discussion of work in progress. The workshops serve as an incubator for the development of important new ideas in this rapidly evolving field. 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 fields of machine learning and neural computation.

For further information about the Series contact:

Michael I. Jordan jordan@cs.berkeley.edu

Thomas G. Dietterich tgd@eecs.oregonstate.edu

*Advanced
Structured Prediction*, Sebastian Nowozin, Peter V. Gehler,
Jeremy Jancsary, and Christoph H. Lampert, eds., 2015

http://mitpress.mit.edu/books/advanced-structured-prediction

*Practical Applications of
Sparse Modeling*, Irina Rish, Guillermo A. Cecchi,
Aurelie Lozano, and Alexandru Niculescu-Mizil, eds., 2014

http://mitpress.mit.edu/books/practical-applications-sparse-modeling

*Optimization for Machine
Learning*, Suvrit Sra, Sebastian Nowozin, and
Stephen J. Wright, eds., 2012

http://mitpress.mit.edu/books/optimization-machine-learning

*Dataset Shift in Machine
Learning*, Joaquin Quiñonero-Candela, Masashi
Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, eds., 2009

http://mitpress.mit.edu/books/dataset-shift-machine-learning

*Learning Machine
Translation*, Cyril Goutte, Nicola Cancedda, Marc
Dymetman, and George Foster, eds., 2009

http://mitpress.mit.edu/books/learning-machine-translation

*Large Scale Kernel Machines*,
Léon Bottou, Olivier Chapelle, Denis DeCoste, and Jason Weston, eds., 2007

http://mitpress.mit.edu/books/large-scale-kernel-machines

*Toward Brain-Computer
Interfacing*, Guido Dornhege, José del R. Millán,
Thilo Hinterberger, Dennis J. McFarland, and Klaus-Robert Müller, eds., 2007

http://mitpress.mit.edu/books/toward-brain-computer-interfacing

*Predicting Structured Data*,
Gökhan Bakır, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben
Taskar, and S. V. N. Vishwanathan, eds., 2007

http://mitpress.mit.edu/books/predicting-structured-data

*New Directions in
Statistical Signal Processing: From Systems to Brains*,
Simon Haykin, José C. Príncipe, Terrence J. Sejnowski, and John McWhirter,
eds., 2007

http://mitpress.mit.edu/books/new-directions-statistical-signal-processing

*Nearest-Neighbor Methods in
Learning and Vision: Theory and Practice*,
Gregory Shakhnarovich, Piotr Indyk, and Trevor Darrell, eds., 2006

http://mitpress.mit.edu/books/nearest-neighbor-methods-learning-and-vision

*Advances in Minimum
Description Length: Theory and Applications*,
Peter D. Grünwald, In Jae Myung, and Mark A. Pitt, eds., 2005

http://mitpress.mit.edu/books/advances-minimum-description-length

*Exploratory Analysis and
Data Modeling in Functional Neuroimaging*,
Friedrich T. Sommer, and Andrzej Wichert, eds., 2003

Out of print, no link

*Probabilistic Models of the
Brain: Perception and Neural Function*,* *Rajesh P. N. Rao, Bruno A. Olshausen,
and Michael S. Lewicki, eds., 2002

http://mitpress.mit.edu/books/probabilistic-models-brain

*Advanced Mean Field
Methods: Theory and Practice*,* *Manfred Opper, and David Saad, eds.,
2001

http://mitpress.mit.edu/books/advanced-mean-field-methods

*Advances in Large Margin
Classifiers*,* *Alexander
J. Smola, Peter L. Bartlett, Bernhard Schölkopf, and Dale Schuurmans, eds.,
2000

http://mitpress.mit.edu/books/advances-large-margin-classifiers