Bayesian Gaussian Process Classification with the EM-EP Algorithm

By Hyun-Chul Kim, Zoubin Ghahramani. 2006

149 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models (2022, background): “In EP with GP (Kim & Ghahramani, 2006), ptarget is the product of a single likelihood factor and all other approximate factors for tractability.”
A Scalable Hierarchical Gaussian Process Classifier (2019, methodology): “Interestingly, HGP even performs better than EP-GP which requires much longer training time and much more memory.”
MCMC for Variationally Sparse Gaussian Processes (2015, methodology): “Here, following [31, 32, 33], we use a robust-max likelihood.”
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation (2019, background): “There have been several early works addressingmulti-class GP classification with a softmax likelihood (Williams and Barber, 1998; Kim and Ghahramani, 2006; Chai, 2012; Riihimäki et al., 2013).”
EEG and MEG Data Analysis in SPM8 (2011, methodology): “(iii) SPM uses a Gaussian process model [34, 35] for source reconstruction based on the sample channel× channel covariance of the data over time.”
Hierarchical Models in the Brain (2008, unknown): “The model in Equation 41 is also referred to as a Gaussian process model [27–29].”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, unknown): “Differently, the multi-class GPC (MGPC) [228] with y ∈ { 1 , · · · , C is f c ∼ GP (0 , k c ) , p ( y f ) = Categorical( π ( f )) , (37) where { f c } Cc =1 are independent latent functions 27 for C classes, and f = [ f 1 , · · · , f C ] T : R d 7→ R C .”
Multiple sparse priors for the M/EEG inverse problem (2008, unknown): “The ensuing approach is related formally to Gaussian process modeling (Ripley, 1994; Rasmussen, 1996; Kim and Ghahramani, 2006), where empirical Gaussian process priors are furnished by a hierarchical (PEB; Kass and Steffey, 1989) model.”
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, methodology): “Alternatively, different forms of probabilistic models have been used to model the label noise and thereby improve various classifiers [57], [58].”