Robust Multi-Class Gaussian Process Classification

69 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning (2021, methodology): “, 2017) which used the Robust-Max likelihood (Hernández-Lobato et al., 2011).”
MCMC for Variationally Sparse Gaussian Processes (2015, methodology): “Here, following [31, 32, 33], we use a robust-max likelihood.”
Doubly Stochastic Variational Inference for Deep Gaussian Processes (2017, methodology): “We use the robust-max multiclass likelihood (Hernández-Lobato et al., 2011) and use full unprocessed data with the standard training/test split of 60K/10K.”
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation (2019, methodology): “Hernández-Lobato et al. (2011) propose the robust-max likelihood p(y = k|f) = (1− ) C∏ c6=y Θ ( fk − f c ) + C , (3) where is the probability of a labeling error, and Θ is the Heaviside function.”
A Scalable Hierarchical Gaussian Process Classifier (2019, methodology): “Here, we take an algorithmically much simpler approach that has been adopted in [36], [39], [44], [45]: using a robust-max function for S.”
Classification in the Presence of Label Noise: A Survey (2014, methodology): “A similar approach is used in [246] to robus-tify multiclass Gaussian process classiﬁcation.”