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
Dataset Shift in Machine Learning
Jun 07, 2022
Log-Linear Models, Extensions, and Applications
Nov 27, 2018
Perturbations, Optimization, and Statistics
Dec 23, 2016
Advanced Structured Prediction
Dec 05, 2014
Practical Applications of Sparse Modeling
Sep 12, 2014
Optimization for Machine Learning
Sep 30, 2011
Nov 14, 2008
Aug 17, 2007
Jul 27, 2007
Toward Brain-Computer Interfacing
Jul 20, 2007
New Directions in Statistical Signal Processing
Oct 13, 2006
Nearest-Neighbor Methods in Learning and Vision
Mar 24, 2006
Advances in Minimum Description Length
Feb 25, 2005
Probabilistic Models of the Brain
Mar 29, 2002
Jun 08, 2001
Advances in Large-Margin Classifiers
Sep 29, 2000