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