Neural Information Processing series

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.

Series editor: Michael I. Jordan and Thomas Dietterich

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Dataset Shift in Machine Learning

Edited by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer and Neil D. Lawrence

Pub Date: Jun 07, 2022

An Introduction to Lifted Probabilistic Inference

Edited by Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan and David Poole

Pub Date: Aug 17, 2021

Log-Linear Models, Extensions, and Applications

Edited by Aleksandr Aravkin, Anna Choromanska, Li Deng, Georg Heigold, Tony Jebara, Dimitri Kanevsky and Stephen J. Wright

Pub Date: Nov 27, 2018

Perturbations, Optimization, and Statistics

Edited by Tamir Hazan, George Papandreou and Daniel Tarlow

Pub Date: Dec 23, 2016

Advanced Structured Prediction

Edited by Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary and Christoph H. Lampert

Pub Date: Dec 05, 2014

Practical Applications of Sparse Modeling

Edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano and Alexandru Niculescu-Mizil

Pub Date: Sep 12, 2014

Optimization for Machine Learning

Edited by Suvrit Sra, Sebastian Nowozin and Stephen J. Wright

Pub Date: Sep 30, 2011

Learning Machine Translation

Edited by Cyril Goutte, Nicola Cancedda, Marc Dymetman and George Foster

Pub Date: Nov 14, 2008

Large-Scale Kernel Machines

Edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston

Pub Date: Aug 17, 2007

Predicting Structured Data

Edited by Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar and S.V.N Vishwanathan

Pub Date: Jul 27, 2007

Toward Brain-Computer Interfacing

Edited by Guido Dornhege, José del R. Millán, Thilo Hinterberger, Dennis J. McFarland and Klaus-Robert Müller

Pub Date: Jul 20, 2007

New Directions in Statistical Signal Processing

Edited by Simon Haykin, Jose C. Principe, Terrence J. Sejnowski and John McWhirter

Pub Date: Oct 13, 2006

Nearest-Neighbor Methods in Learning and Vision

Edited by Gregory Shakhnarovich, Trevor Darrell and Piotr Indyk

Pub Date: Mar 24, 2006

Advances in Minimum Description Length

Edited by Peter D. Grünwald, Jay Injae Myung and Mark A. Pitt

Pub Date: Feb 25, 2005

Exploratory Analysis and Data Modeling in Functional Neuroimaging

Edited by Friedrich T. Sommer and Andrzej Wichert

Pub Date: Nov 08, 2002

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