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How Good are Low-Rank Approximations in Gaussian Process Regression?


Dec 14, 2021
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos

* This submission should be an update of an older arxiv version and not a new one! 

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Entropy-based adaptive Hamiltonian Monte Carlo


Oct 27, 2021
Marcel Hirt, Michalis K. Titsias, Petros Dellaportas

* To appear in NeurIPS (2021) 

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Scalable and Interpretable Marked Point Processes


May 30, 2021
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas


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Faster Gaussian Processes via Deep Embeddings


Apr 03, 2020
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos


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Gradient-based Adaptive Markov Chain Monte Carlo


Nov 04, 2019
Michalis K. Titsias, Petros Dellaportas

* 17 pages, 7 Figures, NeurIPS 2019 

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Copula-like Variational Inference


Apr 15, 2019
Marcel Hirt, Petros Dellaportas, Alain Durmus


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Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers


Sep 20, 2018
Marcel Hirt, Petros Dellaportas

* Additional experiments for linear Gaussian state space models; updated results for Hawkes point process models 

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Fully Scalable Gaussian Processes using Subspace Inducing Inputs


Jul 12, 2018
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias


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