33 citations · 36 across the 4 of their papers we have counts for
4 papers
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular sol…
Kernel Biclustering algorithm in Hilbert Spaces
Marcos Matabuena, J. C Vidal, Oscar Hernan Madrid Padilla +1
Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functi…
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for m…
CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT
Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek +5
In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the kno…