Entry Dependent Experts Aggregation for Local Approximation Gaussian Processes (2022) by Hamed Jalali and Gjergji Kasneci (Under review).
Expert Selection in Distributed Gaussian Processes: A Multi-label Classification Approach by Hamed Jalali and Gjergji Kasneci was accepted at the Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems @NeurIPS 2022 ( Link).
Aggregating the Gaussian Experts' Predictions via Undirected Graphical Models by Hamed Jalali and Gjergji Kasneci was accepted at IEEE BigComp 2022 ( Link).
Model Selection in Local Approximation Gaussian Processes: A Markov Random Fields Approach by Hamed Jalali, Martin Pawelczyk, and Gjergji Kasneci was accepted at IEEE BigData 2021 ( Link).
Gaussian experts selection using graphical models by Hamed Jalali, Martin Pawelczyk, and Gjergji Kasneci , Published on ArXiv (2021) ( Link).
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes by Hamed Jalali and Gjergji Kasneci was accepted at the OPT Workshop @NeurIPS 2021 ( Link).
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines (2021), by Vadim Borisov, Johannes Meier, Johan Van den Heuvel, Hamed Jalali, and Gjergji Kasneci was accepted at the Workshop on eXplainable AI approaches for debugging and diagnosis @NeurIPS 2021 ( Link).
Aggregating Dependent Gaussian Experts in Local Approximation (2020) by Hamed Jalali and Gjergji Kasneci was accepted at ICPR 2020 ( Link).
