Search Results for author: Hamed Jalali

Found 5 papers, 1 papers with code

Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes

no code implementations7 Feb 2022 Hamed Jalali, Gjergji Kasneci

Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction.

Diversity Gaussian Processes

A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines

1 code implementation14 Nov 2021 Vadim Borisov, Johannes Meier, Johan van den Heuvel, Hamed Jalali, Gjergji Kasneci

Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms.

Gaussian Experts Selection using Graphical Models

no code implementations2 Feb 2021 Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci

Imposing the \emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification.

Gaussian Processes Uncertainty Quantification

Aggregating Dependent Gaussian Experts in Local Approximation

no code implementations17 Oct 2020 Hamed Jalali, Gjergji Kasneci

The precision matrix encodes conditional dependencies between experts and is used to detect strongly dependent experts and construct an improved aggregation.

Diversity Gaussian Processes

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