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NIPS
2007
13 years 6 months ago
Robust Regression with Twinned Gaussian Processes
We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over laten...
Andrew Naish-Guzman, Sean B. Holden
NIPS
2003
13 years 6 months ago
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...
JMLR
2010
118views more  JMLR 2010»
12 years 11 months ago
Dirichlet Process Mixtures of Generalized Linear Models
We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models ...
Lauren Hannah, David M. Blei, Warren B. Powell
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 2 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava