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stat.ML2021
How Good are Low-Rank Approximations in Gaussian Process Regression?
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on trun…
stat.ML2021
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to der…