SpAM: Sparse additive models

Pradeep Ravikumar*, Han Liu, John Lafferty, Larry Wasserman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Scopus citations


We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties of SpAM is given together with empirical results on synthetic and real data, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
StatePublished - Dec 1 2009
Event21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada
Duration: Dec 3 2007Dec 6 2007


Conference21st Annual Conference on Neural Information Processing Systems, NIPS 2007
CityVancouver, BC

ASJC Scopus subject areas

  • Information Systems


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