98 citations · 196 across the 32 of their papers we have counts for
5 papers · 1 filter
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…
Entropy-based adaptive Hamiltonian Monte Carlo
Marcel Hirt, Michalis K. Titsias, Petros Dellaportas
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used…
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…
Inference for partially observed Riemannian Ornstein-Uhlenbeck diffusions of covariance matrices
Mai Ngoc Bui, Yvo Pokern, Petros Dellaportas
We construct a generalization of the Ornstein-Uhlenbeck processes on the cone of covariance matrices endowed with the Log-Euclidean and the Affine-Invariant metrics. Our developmen…
Flexible marked spatio-temporal point processes with applications to event sequences from association football
Santhosh Narayanan, Ioannis Kosmidis, Petros Dellaportas
We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively to the space of marks, providing the freedom to s…